Journal of Probability and Statistics 

About: Journal of Probability and Statistics is an academic journal published by Hindawi Publishing Corporation. The journal publishes majorly in the area(s): Estimator & Mean squared error. It has an ISSN identifier of 1687-952X. It is also open access. Over the lifetime, 384 publications have been published receiving 2959 citations. The journal is also known as: JPS.

Analysis of Longitudinal and Survival Data: Joint Modeling, Inference Methods, and Issues

Abstract: In the past two decades, joint models of longitudinal and survival data have received much attention in the literature. These models are often desirable in the following situations: (i) survival models with measurement errors or missing data in time-dependent covariates, (ii) longitudinal models with informative dropouts, and (iii) a survival process and a longitudinal process are associated via latent variables. In these cases, separate inferences based on the longitudinal model and the survival model may lead to biased or inefficient results. In this paper, we provide a brief overview of joint models for longitudinal and survival data and commonly used methods, including the likelihood method and two-stage methods.

Methods for Analyzing Multivariate Phenotypes in Genetic Association Studies

Abstract: Multivariate phenotypes are frequently encountered in genetic association studies. The purpose of analyzing multivariate phenotypes usually includes discovery of novel genetic variants of pleiotropy effects, that is, affecting multiple phenotypes, and the ultimate goal of uncovering the underlying genetic mechanism. In recent years, there have been new method development and application of existing statistical methods to such phenotypes. In this paper, we provide a review of the available methods for analyzing association between a single marker and a multivariate phenotype consisting of the same type of components (e.g., all continuous or all categorical) or different types of components (e.g., some are continuous and others are categorical). We also reviewed causal inference methods designed to test whether the detected association with the multivariate phenotype is truly pleiotropy or the genetic marker exerts its effects on some phenotypes through affecting the others.

The Exponentiated Half-Logistic Family of Distributions: Properties and Applications

Abstract: We study some mathematical properties of a new generator of continuous distributions with two extra parameters called the exponentiated half-logistic family. We present some special models. We investigate the shapes of the density and hazard rate function. We derive explicit expressions for the ordinary and incomplete moments, quantile and generating functions, probability weighted moments, Bonferroni and Lorenz curves, Shannon and Renyi entropies, and order statistics, which hold for any baseline model. We introduce two bivariate extensions of this family. We discuss the estimation of the model parameters by maximum likelihood and demonstrate the potentiality of the new family by means of two real data sets.

Spatial Scan Statistics Adjusted for Multiple Clusters

Abstract: The spatial scan statistic is one of the main epidemiological tools to test for the presence of disease clusters in a geographical region. While the statistical significance of the most likely cluster is correctly assessed using the model assumptions, secondary clusters tend to have conservatively high P-values. In this paper, we propose a sequential version of the spatial scan statistic to adjust for the presence of other clusters in the study region. The procedure removes the effect due to the more likely clusters on less significant clusters by sequential deletion of the previously detected clusters. Using the Northeastern United States geography and population in a simulation study, we calculated the type I error probability and the power of this sequential test under different alternative models concerning the locations and sizes of the true clusters. The results show that the type I error probability of our method is close to the nominal level and that for secondary clusters its power is higher than the standard unadjusted scan statistic.

Optimal Bandwidth Selection for Kernel Density Functionals Estimation

Abstract: The choice of bandwidth is crucial to the kernel density estimation (KDE) and kernel based regression. Various bandwidth selection methods for KDE and local least square regression have been developed in the past decade. It has been known that scale and location parameters are proportional to density functionals with appropriate choice of and furthermore equality of scale and location tests can be transformed to comparisons of the density functionals among populations. can be estimated nonparametrically via kernel density functionals estimation (KDFE). However, the optimal bandwidth selection for KDFE of has not been examined. We propose a method to select the optimal bandwidth for the KDFE. The idea underlying this method is to search for the optimal bandwidth by minimizing the mean square error (MSE) of the KDFE. Two main practical bandwidth selection techniques for the KDFE of are provided: Normal scale bandwidth selection (namely, “Rule of Thumb”) and direct plug-in bandwidth selection. Simulation studies display that our proposed bandwidth selection methods are superior to existing density estimation bandwidth selection methods in estimating density functionals.