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In this article, a large class of univriate Birnbaum–Saunders distributions based on the scale shape mixture of skew normal distributions is introduced which generates suitable subclasses for modeling asymmetric data in a variety of... more
In this article, a large class of univriate Birnbaum–Saunders distributions based on the scale shape mixture of skew normal distributions is introduced which generates suitable subclasses for modeling asymmetric data in a variety of settings. The moments and maximum likelihood estimation procedures are disscused via an ECM-algorithm. The observed information matrix to approximate the asymptotic covariance matrix of the parameter estimates is then derived in some subclasses. A simulation study is also performed to evaluate the finite sample properties of ML estimators and finally, a real data set is analyzed for illustrative purposes.
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Recently, the strong consistency and asymptotic distribution for the maximum consecutive pairwise likelihood estimators (MCPLE) have been established in the linear time series models. In this paper, the weak convergence of the maximum... more
Recently, the strong consistency and asymptotic distribution for the maximum consecutive pairwise likelihood estimators (MCPLE) have been established in the linear time series models. In this paper, the weak convergence of the maximum weighted pairwise likelihood estimator (MWPLE) of the parameters of the AR(1) models is established by using the concept of ܮ ଶ convergence (convergence in mean square).
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Abstract Suppose that a system is affected by a sequence of shocks that occur randomly over time, and δ 1 , δ 2 , η 1 and η 2 are critical levels such that 0 δ 1 δ 2 and 0 η 1 η 2 . In this paper, a new mixed δ -shock model is introduced... more
Abstract Suppose that a system is affected by a sequence of shocks that occur randomly over time, and δ 1 , δ 2 , η 1 and η 2 are critical levels such that 0 δ 1 δ 2 and 0 η 1 η 2 . In this paper, a new mixed δ -shock model is introduced for which the system fails with a probability, say θ 1 , when the time between two consecutive shocks lying in [ δ 1 , δ 2 ] , and the system fails with a probability, say θ 2 , when the magnitude of a shock lying in [ η 1 , η 2 ] . The system fails with probability 1, as soon as the interarrival time between two successive shocks is less than δ 1 or a shock with magnitude greater than η 2 occurs. The corresponding survival function is derived under two scenarios of independence and dependence between the interarrival times and the magnitude of shocks. The first and second moments are also derived. To illustrate the behavior of the system’s lifetime, a simulation study is also conducted.
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In this paper, we propose a new class of continuous distributions with two extra shape parameters called the a new type I half logistic-G family of distributions. Some of important properties including ordinary moments, quantiles, moment... more
In this paper, we propose a new class of continuous distributions with two extra shape parameters called the a new type I half logistic-G family of distributions. Some of important properties including ordinary moments, quantiles, moment generating function, mean deviation, moment of residual life, moment of reversed residual life, order statistics and extreme value are obtained. To estimate the model parameters, the maximum likelihood method is also applied by means of Monte Carlo simulation study. A new location-scale regression model based on the new type I half logistic-Weibull distribution is then introduced. Applications of the proposed family is demonstrated in many fields such as survival analysis and univariate data fitting. Empirical results show that the proposed models provide better fits than other well-known classes of distributions in many application fields.
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We consider the structures of periodically correlated wide-sense Markov (PCWM) processes and their associated multi-dimensional stationary processes. The main result of the paper concerns the structure of multivariate PCWM processes, in... more
We consider the structures of periodically correlated wide-sense Markov (PCWM) processes and their associated multi-dimensional stationary processes. The main result of the paper concerns the structure of multivariate PCWM processes, in terms of multivariate autoregressive and periodic autoregressive processes. But we also correct some results previously obtained for univariate PCWM processes.
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Abstract In this paper, we show that the derivation of Lemma 3 of Das and Dey (2010) needs to be corrected by using a logical transformation, instead of the ad-hoc transformation which is partially motivated by its univariate equivalent... more
Abstract In this paper, we show that the derivation of Lemma 3 of Das and Dey (2010) needs to be corrected by using a logical transformation, instead of the ad-hoc transformation which is partially motivated by its univariate equivalent transformation. The correct derivation is presented by two approaches.
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Microtubule affinity-regulating kinase 4 (MARK4) is a Ser/Thr protein kinase, best known for its role in phosphorylating microtubule associated proteins, causing their detachment from microtubules. In the current study, the... more
Microtubule affinity-regulating kinase 4 (MARK4) is a Ser/Thr protein kinase, best known for its role in phosphorylating microtubule associated proteins, causing their detachment from microtubules. In the current study, the non-phosphorylated conformation of the activation loop was modeled in a structure representing the enzymatically inactive form of this protein, and its dynamics were evaluated through a 100 ns initial all-atom simulation, which was prolonged by another 2 μs. Although the activation loop was folding on itself and was leaning toward ATP site in the initial modeled structure, soon after the initiating the simulation, this loop stretched away from the ATP binding site and stably settled in its new position for the rest of simulation time. A network of hydrogen bonds, mainly between the activation segment residues, αC-helix and the catalytic loop reinforced this conformation. Interestingly, several features of active kinase conformation such as formation of R-spine, G...
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Hydrological processes (models or systems) have both deterministic and stochastic components. In the deterministic models, the state of the systems in time (or space) can be exactly predicted, but in the stochastic models, some random... more
Hydrological processes (models or systems) have both deterministic and stochastic components. In the deterministic models, the state of the systems in time (or space) can be exactly predicted, but in the stochastic models, some random elements are involved. With stochastic analysis, it is possible to calculate time response (delay time) of hydrologic variables in any catchment with respect to rainfall, if we have time series of those variables. Knowing the delay times, we can manage the reservoir behavior more effectively. In this study, we have various hydrologic time series in the Doroudzan Dam area, Fars Province, south of Iran, which involve: mean discharge input to the reservoir, water level changes in the reservoir, water level changes in dam’s piezometers, dam seepage flow changes in downstream (4 components) and rainfall, with time interval for all time series equals to 15 days. For computing the mean delay time (lag time or effective response time) of the various hydrological components with respect to rainfall, phase and coherency curves must be calculated and plotted. It is reported that lag times for input flow to the reservoir, water level in the reservoir, the water level in piezometers (No: 1, 4, 5 and 2) and seepage in the dam embankment are: 0.6, 1.164, 1.43, 1.77, 5.32, 10.3 and 4.7 days, respectively.
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The final prediction error (FPE) criterion is an asymptotic estimate of the prediction error that is used for autoregressive (AR) model order selection. In this paper, we derive a new theoretical estimate of the prediction error for the... more
The final prediction error (FPE) criterion is an asymptotic estimate of the prediction error that is used for autoregressive (AR) model order selection. In this paper, we derive a new theoretical estimate of the prediction error for the same-realization predictions. This estimate is derived for the case that the Least-Squares-Forward (LSF) method (the covariance method) is used as the AR