Scanner Data

The effects of topography on the radiometric properties of multispectral scanner (MSS) data are examined in the context of the remote sensing of forests in mountainous regions. The two test areas considered for this study are located in the coastal mountains of British Columbia, ...

We examine retail and wholesale prices for a large supermarket chain over seven and one-half years. We find that prices fall on average during seasonal demand peaks for a product, largely due to changes in retail margins. Retail margins for specific goods fall during peak demand periods for that good, even if these periods do not coincide with aggregate demand peaks for the retailer. This is consistent with "loss-leader" models of retailer competition. Models stressing cyclical demand elasticities or cyclical firm conduct are less consistent with our findings. Manufacturer behavior plays a limited role in the countercyclicality of prices. (JEL LI 3, E32, L81)

In recent years, manufacturers have become increasingly disposed toward the use of sales promotions, often at the cost of advertising. Yet the long-term implications of these changes for brand profitability remain unclear. In this paper, we seek to offer insights into this important issue. We consider the questions of i) whether it is more desirable to advertise or promote, ii) whether it is better to use frequent, shallow promotions or infrequent, deep promotions, and iii) how changes in regular prices affect sales relative to increases in price promotions. Additional insights regarding brand equity, the relative magnitude of short-and long-term effects, and the decomposition of advertising and promotion elasticities across choice and quantity decisions are obtained.

The data acquired from the hyperspectral airborne sensor DAIS-7915 over Izrael Valley in northern Israel was processed to yield quantitative soil properties maps of organic matter, soil eld moisture, soil saturated moisture, and soil salinity. The method adopted for this purpose was the Visible and Near Infrared Analysis ( VNIRA) approach, which yields an empirical model for predicting the soil property in question from both wet chemistry and spectral information of a representative set of samples (calibration set). Based on spectral laboratory data that show a signi cant capability to predict the above soil properties and populations using the VNIRA strategy, the next step was to examine this feasibility under a hyperspectral remote sensing (HSR) domain. After atmospherically rectifying the DAIS-7915 data and omitting noisy bands, the VNIRA routine was performed to yield a prediction equation model for each property, using the re ectance image data. Applying this equation on a pixel-bypixel basis revealed images that described spatially and quantitatively the surface distribution of each property. The VNIRA results were validated successfully from a priori knowledge of the area characteristics and from data collected from several sampling points. Following these examinations, a procedure was developed in order to create a soil property map of the entire area, including soils under vegetated areas. This procedure employed a random selection of more than 80 points along nonvegetated areas from the quantitative soil property images and interpolation of the points to yield an isocontour map for each property. It is concluded that the VNIRA method is a promising strategy for quantitative soil surface mapping, furthermore, the method could even be improved if a better quality of HSR data were used.

what has been referred to as theory applications (TA) research in which the main focus is on laboratory experiments with student subjects and high internal validity. In this article, the author argues that external validity concerns should be given more attention, particularly in TA research. Three recommendations are made for implementing these concerns: (1) consumer behavior articles should be required to have a section indicating how increased levels of external validity can be obtained with other studies, (2) "joint ventures" between consumer behavior and marketing science researchers can be profitable and should be encouraged, and analyses of electronic scanner panel data or other secondary data can be used to generate higher levels of external validity. Three examples are given from the marketing literature of how findings from experiments and scanner data can be combined to advance a stream of research.

One of the mysteries of store-level scanner data modeling is the lack of a dip in sales in the week(s) following a promotion. Researchers expect to find a postpromotion dip because analyses of household scanner panel data indicate that consumers tend to accelerate their purchases in response to a promotion-that is, they buy earlier and/or purchase larger quantities than they would in the absence of a promotion. Thus, one should also find a pronounced dip in store-level sales in the week(s) following a promotion. However, researchers find such dips usually neither at the category nor at the brand level. Several arguments have been proposed for the lack of a postpromotion dip in store-level sales data. These arguments explain why dips may be hidden. Given that dips are difficult to detect by traditional models (and by a visual inspection of the data), we propose models that can account for a multitude of factors which together cause complex pre-and postpromotion dips. We use three alternative distributed lead-and lag structures: an Almon model, an Unrestricted dynamic effects model, and an Exponential decay model. In each model, we include four types of price discounts: without any support, with display-only support, with feature-only support, and with feature and display support. The models are calibrated on store-level scanner data for two product categories: tuna and toilet tissue. We estimate the dip to be between 4 and 25 percent of the current sales effect, which is consistent with household-level studies.

a b s t r a c t X-ray fluorescence (XRF) scanning of unlithified, untreated sediment cores is becoming an increasingly common method used to obtain paleoproxy data from lake records. XRF-scanning is fast and delivers high-resolution records of relative variations in the elemental composition of the sediment. However, lake sediments display extreme variations in their organic matter content, which can vary from just a few percent to well over 50%. As XRF scanners are largely insensitive to organic material in the sediment, increasing levels of organic material effectively dilute those components that can be measured, such as the lithogenic material (the closed-sum effect). Consequently, in sediments with large variations in organic material, the measured variations in an element will to a large extent mirror the changes in organic material. It is therefore necessary to normalize the elements in the lithogenic component of the sediment against a conservative element to allow changes in the input of the elements to be addressed. In this study we show that Al, which is the lightest element that can be measured using the Itrax XRF-scanner, can be used to effectively normalize the elements of the lithogenic fraction of the sediment against variations in organic content. We also show that care must be taken when choosing resolution and exposure time to ensure optimal output from the measurements.

Using supermarket scanner data, we test a variety of hypotheses from trade journals about the invasion of private-label food products. According to conventional industry wisdom, name-brand firms defended their brands against new private-label products by lowering their prices, engaging in additional promotional activities, and increasingly differentiating their products. Our empirical evidence is inconsistent with these beliefs.

Management of brand equity has come to be viewed as critical to a brand's optimal long-term performance. The authors evaluate the usefulness of brand equity estimates obtained from store-level data for monitoring the health of a brand. They use a random coefficients logit demand model calibrated on store-level scanner data to track brand equity estimates over time in two consumer packaged goods categories that experienced several new product introductions during the period of the empirical investigation. Using these tracked measures, the authors also study the impact of marketing actions, such as advertising, sales promotions, and product innovations, on brand equity. They find that the brand equity estimates effectively capture the high equity of strongly positioned popular brands and brands that command a significant price premium in niche markets. Using an example, the authors illustrate how these brand equity estimates can be used to monitor changes in brand equity, which measures such as market share may fail to capture. The substantive results indicate that advertising has a positive effect on brand equity in both the product categories, whereas the effect of sales promotions is not significant in either category. Furthermore, the results reveal that new product innovations have a positive impact on brand equity and can explain a significant proportion of its variation. Overall, the analysis shows that a brand manager can track brand equity using store-level data, gain insights into the drivers of the brand's equity, and manage these drivers to achieve brand equity targets.

We empirically compare the forecasting ability of artificial neural network (ANN) with multinomial logit model (MNL) in the context offrequently purchased grocery products for a retailer. Using scanner data on three grocery product categories, we find that performunce of ANN compares favorably to MNL in forecasting brand shares. We test the sensitivity of the forecasting error in the two approaches to the length of the estimation period and the clustering of households which is used to define homogenous segments of households. We find the results to be robust to these variations. We also derive a few empirical propositions regarding performance of ANN and MNL from our analysis. The results u-e consistent with those in Kumar, Rao and Soni (1995) and suggest that although neural networks suffer ,from interpretability problem, they are a useful method to forecast brand shares in grocery product categories where large amounts of scanner data are readily available. Artificial neural network (ANN) models are increasingly being used as a decision aid in a number of areas such as manufacturing, marketing, and retailing. Some applications in manufacturing have been in systems design, capacity planning, and product quality control (see Huang and Zhang, 1994 for a review of ANN applications in manufacturing). In marketing, applications have been reported in the areas of customer service, segmentation, and prospect identification (Shepard and Ratner, 1994; Venugopal and Baets, 1994). Some applications have also been reported in retailing particularly in fashion forecasting, retail assortment planning, and retail inventory management (Belt, 1993; Dragstedt, 1991). Retailers are also exploring neural networks to forecast retail demand in the grocery products industry (Thall, 1992). The use of neural networks in grocery retailing is especially appealing because large amounts of UPC (universal product code) scanner data routinely become available from the electronic check-out systems installed in the retail stores. Accurate demand forecasting is crucial for profitable retail operations because without a good

Four multi-method studies show that the more elongated a container, the lower its purchase quantity. Study 1, in the lab, shows beer bottles are perceived to contain more than beer cans, particularly for infrequent beer drinkers. Study 2 analyzes scanner data to show that the purchase quantity of cans is 63.66 percent higher than the purchase quantity of bottles. Study 3, a virtual shopping survey, demonstrates these effects are strongest when the context is socializing at home, and Study 4, in the lab, shows results hold only when desired consumption level is constant. Implications for retailers and product managers are offered.

This paper uses over two years of weekly scanner data from two small US cities to characterize time and state dependence of grocers' pricing decisions. In these data, the probability of a nominal adjustment declines with the time since the last price change even after controlling for heterogeneity across store-product cells and replacing sale prices with regular prices. We also detect state dependence: The probability of a nominal adjustment is highest when a store's price substantially differs from the average of other stores' prices. However, extreme prices typically reflect the selling store's recent nominal adjustments rather than changes in other stores' prices.

Stimulation of the vagus nerve in the neck can reduce seizures in epilepsy patients, and may be helpful in treating depression. PET studies have shown that vagus nerve stimulation (VNS) in epilepsy patients causes acute dose (intensity) dependent changes in regional cerebral blood flow. We sought to use the newly developed VNS synchronized fMRI technique to examine whether VNS BOLD signal changes depend on the frequency of stimulation. Six adults with recurrent depression were scanned inside a 1.5 T MR scanner. Data were acquired at rest, with the VNS device on for 7 s, and also, for comparison, while the patient listened to a tone for 7 s. In two separate back-to-back sessions, the VNS stimulation frequency was set to either 5 or 20 Hz. Data were transformed into Talairach space and then compared by condition. Compared to 5 Hz, 20 Hz VNS produced more acute activity changes from rest in regions similar to our initial VNS synchronized fMRI feasibility study in depression. Brain regions activated by hearing a tone were also greater when VNS was intermittently being applied at 20 Hz than at 5 Hz. In depressed adults, left cervical VNS causes regional brain activity changes that depend on the frequency of stimulation or total dose, or both. In addition to the acute immediate effects of VNS on regional brain activity, this study suggests further that VNS at different frequencies likely has frequency or dose dependent modulatory effects on other brain activities (e.g. hearing a tone). # (M. Lomarev).

In this paper we introduce a new flexible mixed model for multinomial discrete choice where the key individual-and alternative-specific parameters of interest are allowed to follow an assumptionfree nonparametric density specification while other alternative-specific coefficients are assumed to be drawn from a multivariate normal distribution which eliminates the independence of irrelevant alternatives assumption at the individual level. A hierarchical specification of our model allows us to break down a complex data structure into a set of submodels with the desired features that are naturally assembled in the original system. We estimate the model using a Bayesian Markov Chain Monte Carlo technique with a multivariate Dirichlet Process (DP) prior on the coefficients with nonparametrically estimated density. We employ a "latent class" sampling algorithm which is applicable to a general class of models including non-conjugate DP base priors. The model is applied to supermarket choices of a panel of Houston households whose shopping behavior was observed over a 24-month period in years 2004-2005. We estimate the nonparametric density of two key variables of interest: the price of a basket of goods based on scanner data, and driving distance to the supermarket based on their respective locations. Our semi-parametric approach allows us to identify a complex multi-modal preference distribution which distinguishes between inframarginal consumers and consumers who strongly value either lower prices or shopping convenience.

The purpose of this paper is to examine the dynamic effects of sales promotions. We create dynamic brand sales models (for weekly store-level scanner data) by relating store intercepts and a brand's own price elasticity to a measure of the cumulated previous price discountsamount and timefor that brand as well as for other brands. The brand's own non-price promotional response parameters are related to the time since the most recent promotion for that brand as well as for other brands. We demonstrate that these dynamic econometric models provide greater managerial relevance than static models can.

This article uses revealed preferences of consumers to study the consumer benefits from rBST-free and organic labeled milk. The article specifies and estimates a quadratic AIDS demand system model for different milk types using US supermarket scanner data. The introduction of rBST-free and organic milk is used to estimate consumer benefits, which are decomposed into two components, competitive and variety effects. Results show significant consumer benefits from organic milk and to a lesser extent from rBST-free milk. Based on the findings, we explore implications for present U.S. labeling standards.

This study characterises the ‘deal-proneness’ of consumers by analysis of the consumer-level characteristics of price sensitivity and brand loyalty. The study first develops a multinomial logistic (MNL) latent class model suitable for use with universal product code (UPC) point-of-sale (hypermarket) scanner data. The model is then used to assess the deal-proneness of consumers with respect to monetary promotions (price reductions) and non-monetary promotions (store flyers). The results show that almost 47% of consumers can be considered deal-prone, in that both kinds of sales promotions have a significant effect on their choice behaviour. The findings provide important insights for retail management in seeking to optimise the results obtained from promotional budgets.

Neuroimaging studies in bipolar disorder report gray matter volume (GMV) abnormalities in neural regions implicated in emotion regulation, including ventral/orbital medial prefrontal cortex (OMPFC) GMV decreases and, more inconsistently, amygdala GMV increases. We aimed to examine OMPFC and amygdala GMV in bipolar disorder, type 1 patients (BPI) versus healthy control participants (HC), and examine potential confounding effects of gender, clinical and illness history variables and psychotropic medication upon any group differences that were demonstrated in OMPFC and amygdala GMV. Images were acquired from 27 BPI (17 euthymic, 10 depressed) and 28 age-and gender-matched HC in a 3T Siemens scanner. Data were analyzed with SPM5 using voxel-based morphometry to first examine main effects of diagnostic group and gender upon whole brain (WB) GMV. Post hoc analyses were subsequently performed to examine the extent to which clinical and illness history variables and psychotropic medication contributed to GMV abnormalities in BPI in a priori and non-a priori regions demonstrated by the above VBM analyses. Here, SPPSS was used to examine the effects of these variables on magnitude of GMV in these a priori and nona priori regions in BPI versus HC. BPI showed reduced GMV in two regions established a priori: bilateral posteromedial rectal gyrus (PMRG), but no amygdala GMV abnormalities. BPI also showed reduced GMV in two non-a priori regions: left parahippocampal gyrus and left putamen. For left PMRG GMV, there was a significant group by gender by trait anxiety interaction. GMV was significantly reduced in male low trait anxiety BPI versus male low trait anxiety HC, and in high versus low trait anxiety male BPI. Our findings show in BPI significant effects of male gender and high trait anxiety on GMV reduction in left PMRG, part of the OMPFC medial prefrontal network implicated in visceromotor and emotion regulation.

B rain-computer interfaces (BCIs) utilize neurophysiological signals originating in the brain to activate or deactivate external devices or computers . Different neuroelectric signals have been used to control external devices, including EEG oscillations, electrocorticograms (ECoGs) from implanted electrodes, event-related potentials (ERPs) such as the P300 and slow cortical potential (SCP), short latency subcortical potentials and visually evoked potentials, and action potential spike trains from implanted multielectrodes. In comparison, the development of BCIs based on metabolic activity of the brain using two different imaging methods, functional magnetic resonance imaging (fMRI) [2] and functional near infrared spectroscopy [3], has been more recent.

Asymmetric pricing is the phenomenon where prices rise more readily than they fall. We articulate, and provide empirical support for, a theory of asymmetric pricing in wholesale prices. In particular, we show how wholesale prices may be asymmetric in the small but symmetric in the large, when retailers face costs of price adjustments. Such retailers will not adjust prices for small changes in their costs. Upstream manufacturers then see a region of inelastic demand where small wholesale price changes do not translate into commensurate retail price changes. The implication is asymmetric -small wholesale increases are more profitable because manufacturers will not lose customers from higher retail prices; yet, small wholesale decreases are less profitable, because these will not create lower retail prices, hence no extra revenue from greater sales. For larger changes, this asymmetry at wholesale vanishes as the costs of changing prices are compensated by increases in retailers' revenue that result from correspondingly large retail price changes. We first present a formal economic model of a channel with forward looking retailers facing costs of price adjustment to derive the testable propositions. Next, we test these on manufacturer prices in a supermarket scanner dataset to find support for our theory. We discuss the contributions of the results for the asymmetric pricing, distribution channels and cost of price adjustment literatures, and implications for public policy.

This paper outlines the potential use of bar-code scanner data from retailers for the measurement of in¯ation. The source bene®ts from its extensive coverage in providing data on prices, quantities and values of transactions of each model of a good sold. Relative weights can thus be ascribed to price changes in both base and current months at a highly detailed level which allows us to estimate substitution bias. Methods of adjusting for quality changes can be considered. The dummy variable hedonic approach is compared with a superlative, exact hedonic approach and a matching technique akin to that used by statistical of®ces.

New technologies for processing transactions, together with increases in the ability to store and process information, provide tremendous opportunities for measurement. When data are based on actual transactions, as opposed survey samples of price quotations, revenues, and expenditures, there is the potential for measurement to closely reflect the underlying variable being measured. Basing data on actual transactions also creates opportunities for modeling the behavior underlying the transactions. Such analysis can both improve measurement and be used to study a wide range of economic phenomena.

The use of 3D digitization and modeling in documenting heritage sites has increased significantly over the past few years. This is mainly due to advances in laser scanning techniques, 3D modeling software, image-based-modeling techniques, computer power, and virtual reality. There are many approaches currently available. The most common remains based on surveying and CAD tools and/or traditional photogrammetry with control points and a human operator. This is very time consuming and can be tedious and sustained effort. Lately, modeling methods based on scanners data and more automated image-based technique are becoming available. We will discuss each approach and point out its advantages and disadvantages. We will then present our approach, which is a combination of several technologies. The approach presented in this paper uses both interactive and automatic techniques, each where it is best suited, to accurately and completely model heritage objects and sites. A highly detailed st...

The workhorse brand choice models in marketing are the multinomial logit (MNL) and nested multinomial logit (NMNL). These models place strong restrictions on how brand share and purchase incidence price elasticities are related. In this paper, we propose a new model of brand choice, the "price consideration" (PC) model, that allows more flexibility in this relationship. In the PC model, consumers do not observe prices in each period. Every week, a consumer decides whether to consider a category. Only then does he/she look at prices and decide whether and what to buy. Using scanner data, we show the PC model fits much better than MNL or NMNL. Simulations reveal the reason: the PC model provides a vastly superior fit to inter-purchase spells.

Rapidly available and accurate information about the location and extent of avalanche events is important for avalanche forecasting, safety assessments for roads and ski resorts, verification of warning products, as well as for hazard mapping and avalanche model calibration/validation. Today, observations from individual experts in the field provide isolated information with very limited coverage. This study presents a methodology for an automated, systematic and wide-area detection and mapping of avalanche deposits using optical remote sensing data of high spatial and radiometric resolution. A processing chain, integrating directional, textural and spectral information, is developed using ADS40 airborne digital scanner data acquired over a test site near Davos, Switzerland. Though certain limitations exist, encouraging detection and mapping accuracies can be reported. The presented approach is a promising addition to existing field observation methods for remote regions, and can be applied in otherwise inaccessible areas.

Airborne laser scanners enable the geometric acquisitionof the terrain surface, including objects liketrees or buildings which rise from the terrain. Eventhough for a number of applications a so#called DigitalSurface Model #DSM# representing the surfacegeometry by an object independent distribution ofpoints is su#cient, the further quali#cation of theoriginal scanner data is necessary for more sophisticatedtasks like visualizations or high quality3Dsimulations.

Cognitive deficits in Huntington's disease (HD) have been attributed to neuronal degeneration within the striatum; however, postmortem and structural imaging studies have revealed more widespread morphological changes. To examine the impact of HD-related changes in regions outside the striatum, we used functional magnetic resonance imaging (fMRI) in HD to examine brain activation patterns using a Simon task that required a button press response to either congruent or incongruent arrow stimuli. Twenty mild to moderate stage HD patients and 17 healthy controls were scanned using a 3 T GE scanner. Data analysis involved the use of statistical parametric mapping software with a random effects analysis model to investigate group differences brain activation patterns compared to baseline. HD patients recruited frontal and parietal cortical regions to perform the task, and also showed significantly greater activation, compared to controls, in the caudal anterior cingulate, insula, inferior parietal lobules, superior temporal gyrus bilaterally, right inferior frontal gyrus, right precuneus/superior parietal lobule, left precentral gyrus, and left dorsal premotor cortex. The significantly increased activation in anterior cingulate-frontal-motor-parietal cortex in HD may represent a primary dysfunction due to direct cell loss or damage in cortical regions, and/or a secondary compensatory mechanism of increased cortical recruitment due to primary striatal deficits.

We study the optimal levels of advertising and promotion budgets in dynamic markets with brand equity as a mediating variable. To this end, we develop and estimate a state-space model based on the Kalman filter that captures the dynamics of brand equity as influenced by its drivers that include a brand's advertising and sales promotion expenditures. By integrating the Kalman filter with the random coefficients logit demand model, our estimation allows us to capture the dynamics of brand equity as well as model consumer heterogeneity using store-level data. Using these demand model estimates, we determine the Markov Perfect Equilibrium advertising and promotion strategies. Our empirical analysis is based on store-level scanner data in the orange juice category, which comprises two major brands -Tropicana and Minute Maid. The calibration of the demand model reveals that sales promotions have a significant positive effect on consumers' utility and induce consumers to switch to the promoted brand. However, there is also a negative effect of promotions on brand equity that carries over from period to period. Overall, we find that while sales promotions have a net positive impact both in the short-term and in the long-term, the implied total elasticity including the long-term effect is smaller than the short-term elasticity. Correspondingly, we expect myopic decision-makers to allocate higher than optimal expenditures to sales promotions. Our results from the supply side analysis reveal that the equilibrium forward-looking promotion levels are higher for Minute Maid, the brand for which the adverse long-term effect of promotions is lower. However, the observed promotion levels are higher for Tropicana compared to Minute Maid, a result consistent only with the myopic case. Further, our results reveal that the actual promotion levels for both brands are higher than the optimal budgets for the forward-looking as well as the two-year planning horizon scenarios. Hence, it may be profitable for both brands to reduce their promotion levels. The equilibrium forward-looking advertising levels are higher for Tropicana, the brand that has a higher responsiveness to advertising. Further, the optimal forward-looking advertising levels are higher than the optimal budgets for the myopic as well as the two-year planning horizon scenarios. However, these levels are higher than the actual advertising expenditures for both brands. Hence, it may be optimal for both brands to reduce their advertising levels.

Positron emission tomography (PET) is a non-invasive molecular imaging technique using positron-emitting radioisotopes to study functional processes within the body. High resolution PET scanners designed for imaging rodents and non-human primates are now commonplace in preclinical research. Brain imaging in this context, with motion compensation, can potentially enhance the usefulness of PET by avoiding confounds due to anaesthetic drugs and enabling freely moving animals to be imaged during normal and evoked behaviours. Due to the frequent and rapid motion exhibited by alert, awake animals, optimal motion correction requires frequently sampled pose information and precise synchronisation of these data with events in the PET coincidence data stream. Motion measurements should also be as accurate as possible to avoid degrading the excellent spatial resolution provided by state-of-the-art scanners. Here we describe and validate methods for optimised motion tracking suited to the correction of motion in awake rats. A hardware based synchronisation approach is used to achieve temporal alignment of tracker and scanner data to within 10 ms. We explored the impact of motion tracker synchronisation error, pose sampling rate, rate of motion, and marker size on motion correction accuracy. With accurate synchronisation (,100 ms error), a sampling rate of .20 Hz, and a small head marker suitable for awake animal studies, excellent motion correction results were obtained in phantom studies with a variety of continuous motion patterns, including realistic rat motion (,5% bias in mean concentration). Feasibility of the approach was also demonstrated in an awake rat study. We conclude that motion tracking parameters needed for effective motion correction in preclinical brain imaging of awake rats are achievable in the laboratory setting. This could broaden the scope of animal experiments currently possible with PET.

Landmark-based navigation of autonomous mobile robots or vehicles has been widely adopted in industry. Such a navigation strategy relies on identification and subsequent recognition of distinctive environment features or objects that are either known a priori or extracted dynamically. This process has inherent difficulties in practice due to sensor noise and environment uncertainty. This paper is to propose a navigation algorithm that simultaneously locates the robots and updates landmarks in a manufacturing environment. A key issue being addressed is how to improve the localization accuracy for mobile robots in a continuous operation, in which the Kalman filter algorithm is adopted to integrate odometry data with scanner data to achieve the required robustness and accuracy. The Kohonen neural networks have been used to recognize landmarks using scanner data in order to initialize and recalibrate the robot position by means of triangulation when necessary.

Pioneering observations of the diffuse HeI-58.4 nm background radiation were performed with a series of satellites in the 70's. Today, their published results on the flow of interstellar helium atoms in the heliosphere are still in contradiction with (i) the results of the particle experiments, i.e. in situ detection of neutrals and pickup ions; (ii) expectations from heliospheric models and comparison with the hydrogen flow; (iii) results of the recent helium glow observations with the Extreme Ultraviolet Explorer (EUVE). Here we discuss these data sets and their modeling, together with the EUVE data and the first coronographic observations of the helium glow obtained with the Ultraviolet Coronographic Spectrometer (UVCS) on board SOHO. We show how they can all be made compatible, and reconciled with in situ data. We have reanalysed the Prognoz data and we derive an updated and higher value of the background noise level. Based on this we can now fit the data satisfactorily with the same set of helium parameters as that one derived from recent EUVE and in situ data. We suggest that other early data sets could be reanalyzed in the same way. Using this updated analysis, EUVE and SOHO-UVCS measurements, we find that all glow data are compatible with the interstellar parameters V 0 ≈ 25 km s −1 , T 0 ≈ 6500 K, λ 0 ≈ 74.0 deg, β 0 ≈ 6.0 deg (downwind axis, ecliptic coordinates), as well as with the solar parameters derived from SOHO CELIAS-SEM, SUMER and CDS observations, i.e. the helium photoionisation rate, the 58.4 nm irradiance, and the 58.4 nm Doppler width, found to be between 60 and 90 mÅ (30 and 45 km s −1 ). The density is the least constrained parameter from the glow measurements. Prognoz lateral scans, EUVE LWS and SOHO UVCS data are compatible with an interstellar helium density n 0 in the range 0.013-0.016 cm −3 . Prognoz anti-solar data and EUVE scanner data lead to a 40% lower value, suggesting uncertainties in the calibrations. A large part of the contradictions between particle and remote sensing results are thus removed, since the above parameters are very similar to those derived from in situ data. The high electron impact rates inferred from the UVCS remote sensing observations imply high fluxes of newly produced helium pickup ions, which can possibly explain in part the observed correlation between H + and He + pickup fluxes, and the inverse correlation between He + fluxes and solar wind velocity.

A criticism of purchase-based brand loyalty measures is that they are confounded by the marketing mix variables that affect brand choice. This paper investigates the magnitude and direction of the associations for share of category requirements (SCR), defined as each brand's share among the group of households who bought the brand at least once during the time period under consideration.

This paper employs a nation-wide sample of supermarket scanner data to estimate a large brand-level demand system for beer in the U.S. using the Distance Metric method of Pinkse, Slade and Brett [Pinkse, J., Slade, M., Brett, C., 2002. Spatial price competition: a semiparametric approach. Econometrica 70, . Unlike previous studies, this work estimates the own-and cross-advertising elasticities in addition to price elasticities. Positive and negative cross-advertising elasticities imply the presence of both cooperative and predatory effects of advertising expenditures across brands; however, the former effect appears to dominate suggesting that advertising increases the overall demand for beer. We discuss the implications of these results in this industry.

While a significant literature has emerged recently on the longer-term effects of price promotions, as inferred from persistence models, there is very little if any attention paid to whether such longer-term effects vary across different types of consumers. This paper takes a first step in that direction by exploring whether the adjustment, permanent, and total effects of price promotions, and the duration of the adjustment period, differ between consumers segmented based on their usage rates in a product category and their loyalty to a brand. We also investigate whether such consumer segmentation will improve the forecasting performance of persistence models at both product category and brand levels. Expectations are developed based on consumer behavior theory on various effects of price promotions, such as the post-deal trough, the mere purchase effect, the promotion usage effect, and responsiveness to competitor's reactions. Evidence from household-level supermarket scanner data on four product categories is provided. We find substantial differences between consumer segments and provide insights on how managers can increase the longer-term effectiveness of price promotions by targeting each consumer segment with a different promotion program. In addition, consumer segmentation is found to significantly improve the forecasting performance of the persistence model for two of the four product categories. For the other two product categories, consumer segmentation provides forecasting performance similar to that obtained from aggregate-level persistence models.