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Performance evaluation of nursing homes using finite mixtures of logistic models and M-quantile regression for binary data
Evaluating the performance of health care institutions is of paramount interest and it is often conducted using generalized linear mixed models. In...
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Oracle-efficient M-estimation for single-index models with a smooth simultaneous confidence band
Single-index models are important and popular semiparametric models, as they can handle the problem of the “curse of dimensionality” and enjoy the...
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Bayesian diagnostics in a partially linear model with first-order autoregressive skew-normal errors
This paper studies a Bayesian local influence method to detect influential observations in a partially linear model with first-order autoregressive...
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Empirical likelihood change point detection in quantile regression models
Quantile regression is an extension of linear regression which estimates a conditional quantile of interest. In this paper, we propose an empirical...
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Estimation and backtesting of risk measures with emphasis on distortion risk measures
Statistical methodology has an important role to play in risk measurement. In this paper, we will review and discuss some statistical issues on risk...
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Additive partial linear models with autoregressive symmetric errors and its application to the hospitalizations for respiratory diseases
Additive partial linear models with symmetric autoregressive errors of order p are proposed in this paper for modeling time series data....
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Profile quasi-maximum likelihood estimation for semiparametric varying-coefficient spatial autoregressive panel models with fixed effects
This paper aims to propose a profile quasi-maximum likelihood estimation method for semiparametric varying-coefficient spatial autoregressive(SVCSAR)...
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A spatio-temporal model for binary data and its application in analyzing the direction of COVID-19 spread
It is often of primary interest to analyze and forecast the levels of a continuous phenomenon as a categorical variable. In this paper, we propose a...
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Robust variable selection for additive coefficient models
Additive coefficient models generalize linear regression models by assuming that the relationship between the response and some covariates is linear,...
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Scalable Bayesian p-generalized probit and logistic regression
The logit and probit link functions are arguably the two most common choices for binary regression models. Many studies have extended the choice of...
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Forecasting multidimensional autoregressive time series model with symmetric \(\alpha\)-stable noise using artificial neural networks
Artificial neural networks have been widely studied and applied in time series forecasting. However, the existing studies focus more on the...
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Two-Part Mixed Effects Mixture Model for Zero-Inflated Longitudinal Compositional Data
Compositional data (CD) is mostly analyzed using ratios of components and log-ratio transformations to apply known multivariable statistical methods....
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A New Regression Model for Over-Dispersed Count Responses Based on Poisson and Geometric Convolution
This article presents an alternative generalized linear regression model specifically designed for count responses that exhibit over-dispersion. The...
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Inference on Weibull inverted exponential distribution under progressive first-failure censoring with constant-stress partially accelerated life test
Accelerated life tests (ALTs) play a pivotal role in life testing experiments as they significantly reduce costs and testing time. Hence, this paper...
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Family of Generalized Symmetric Distributions: Properties and Applications
Generalized distributions are useful for applied statisticians, and some of the popular distributions can be extended in several ways. In this study,...
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Mean test for high-dimensional data based on covariance matrix with linear structures
In this work, the mean test is considered under the condition that the number of dimensions p is much larger than the sample size n when the...
