The concordance filter: an adaptive model-free feature screening procedure
A new model-free and data-adaptive feature screening procedure referred to as the concordance filter is developed for ultrahigh-dimensional data. The proposed method is based on the concordance filter which measures concordance between random ...
An algorithm for generating efficient block designs via a novel particle swarm approach
The problem of finding optimal block designs can be formulated as a combinatorial optimization, but its resolution is still a formidable challenge. This paper presents a general and user-friendly algorithm, namely Modified Particle Swarm ...
Computing T-optimal designs via nested semi-infinite programming and twofold adaptive discretization
Modelling real processes often results in several suitable models. In order to be able to distinguish, or discriminate, which model best represents a phenomenon, one is interested, e.g., in so-called T-optimal designs. These consist of the (design)...
Jackknife empirical likelihood based diagnostic checking for Ar(p) models
Diagnostic checking is an important predefined step before using autoregressive models. Although many portmanteau tests were proposed for diagnostic checking, they still struggle with the issue of significant size distortion. In this paper, we ...
Row mixture-based clustering with covariates for ordinal responses
Existing methods can perform likelihood-based clustering on a multivariate data matrix of ordinal data, using finite mixtures to cluster the rows (observations) of the matrix. These models can incorporate the main effects of individual rows and ...
A CNN-based multi-level face alignment approach for mitigating demographic bias in clinical populations
The investigation of demographic bias in facial analysis applications is a topic of growing interest with achievements in face recognition and gender classification. State-of-the-art convolutional neural networks (CNN) and traditional machine ...
A pth-order random coefficients mixed binomial autoregressive process with explanatory variables
To capture the higher-order autocorrelation structure for finite-range integer-valued time series of counts, and to consider the driving effect of covariates on the underlying process, this paper introduces a pth-order random coefficients mixed ...
A Monte Carlo permutation procedure for testing variance components in generalized linear regression models
Testing zero variance components is of utmost importance in various applications empowered by the use of mixed-effects models. Focusing on generalized linear models, this article proposes a permutation test using an analogue of the ANOVA test ...
Goodness-of-fit procedure for gamma processes
Gamma processes are commonly used for modelling the accumulative deterioration of systems, in many fields. However, given a series of observations, it is not always easy to affirm that the choice of a gamma process modelling is a good choice. In ...
GOLFS: feature selection via combining both global and local information for high dimensional clustering
It is important to identify the discriminative features for high dimensional clustering. However, due to the lack of cluster labels, the regularization methods developed for supervised feature selection can not be directly applied. To learn the ...
Classifying for images based on the extracted probability density function and the quasi Bayesian method
This study presents a novel algorithm for image classification based on a quasi-Bayesian approach and the extraction of probability density functions (PDFs). First, representative PDFs are extracted from each image using its features. Next, a ...
Extremal index: estimation and resampling
The duration of extremes in time leads to a phenomenon known as clustering of high values, with a strong impact on risk assessment. The extremal index is a measure developed within Extreme Value Theory that quantifies the degree of clustering of ...
Using nomination sampling in estimating the area under the ROC curve
The area under a receiver operating characteristic (ROC) curve is frequently used in medical studies to evaluate the effectiveness of a continuous diagnostic biomarker, with values closer to one indicating better classification. Unfortunately, the ...
A new approach to modeling the cure rate in the presence of interval censored data
We consider interval censored data with a cured subgroup that arises from longitudinal followup studies with a heterogeneous population where a certain proportion of subjects is not susceptible to the event of interest. We propose a two component ...
Weighted high dimensional data reduction of finite element features: an application on high pressure of an abdominal aortic aneurysm
In this work we propose a low rank approximation of areal, particularly three dimensional, data utilizing additional weights. This way we enable effective compression if additional information indicates that parts of the data are of higher ...
Inference for a constant-stress model under progressive type-II censored data from the truncated normal distribution
In this study, constant-stress accelerated life testing has been investigated using type-II censoring of failure data from a truncated normal distribution. Various classical estimation approaches are discussed for estimating model parameters, ...
Confidence sub-contour box: an alternative to traditional confidence intervals
Parameter and initial conditions (factors) estimation is a challenging task in non-linear models. Even if researchers successfully estimate those model factors, they still should estimate their confidence intervals, which could require a high ...
A bootstrap test for threshold effects in a diffusion process
This paper proposes a bootstrap testing approach based on an approximate maximum likelihood method to discern whether a diffusion process is linear or whether there are threshold effects in the drift, the diffusion term or in both. It complements ...
On testing the equality between interquartile ranges
The interquartile range is a statistical measure well suited to describe the variability of the data at hand, both at the population level and for sample data. The interquartile range is particularly useful when the distribution of the data is ...
