Journal of Statistical Theory and Practice
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A Skew-Normal Bayesian Semi-parametric Latent Trait Linear Mixed Effect Model
Journal of Statistical Theory and Practice - - 2024
Clinical trials have longitudinally collected the biomarkers that may be
associated with the time-to-event endpoints via latent variables such as disease
severity. These longitudinal data may consist of different types of
measurements. The multilevel item response theory (MLIRT) model is widely used
in several fields including public health and health sciences, for these
longitudinal outcomes. How...
Estimation of Parameters of the Unified Skew Normal Distribution Using the Method of Weighted Moments
Journal of Statistical Theory and Practice - Tập 6 - Trang 402-416 - 2012
Modeling skewness based on the class of skew normal distributions has drawn
considerable attention in recent years. However, there still remain lots of
challenges related to the inferences about the parameters of the skew normal
distribution. In this article, we study the weighted moments estimators for the
unified skew normal distribution. Our analytical results and numerical
illustrations show t...
Optimality of Some Row–Column Designs
Journal of Statistical Theory and Practice - Tập 17 - Trang 1-25 - 2023
Latin squares, Youden squares, and Generalized Youden designs are optimal
row–column designs sharing a common characteristic: in each case the two
component block designs determined by rows and columns are restricted to the
special types of balanced block design known as BIBDs, RCBDs, or more generally
BBDs. This article takes up the optimality problem when it is possible to have a
BIBD column com...
Fast Bayesian Estimation for VARFIMA Processes with Stable Errors
Journal of Statistical Theory and Practice - Tập 4 - Trang 663-677 - 2010
We present an approach via a multivariate preconditioned conjugate gradient
(MPCG) algorithm for Bayesian inference for vector ARFIMA models with
sub-Gaussian stable errors. This approach involves solution of a block-Toeplitz
system, and treating the unobserved process history and the underlying positive
stable process as unknown parameters in the joint posterior. We use Gibbs
sampling with the Me...
Analytical-Numeric Formulas for the Probability Density Function of Multivariate Stable and Geo-Stable Distributions
Journal of Statistical Theory and Practice - - 2014
On the accuracy of fixed sample and fixed width confidence intervals based on the vertically weighted average
Journal of Statistical Theory and Practice - Tập 11 - Trang 375-392 - 2017
Vertically weighted averages perform a bilateral filtering of data, in order to
preserve fine details of the underlying signal, especially discontinuities such
as jumps (in one dimension) or edges (in two dimensions). In homogeneous regions
of the domain the procedure smoothes the data by averaging nearby data points to
reduce the noise, whereas in inhomogeneous regions the neighboring points are
...
Estimating the Mean of High Valued Observations in High Dimensions
Journal of Statistical Theory and Practice - Tập 2 - Trang 407-418 - 2008
Let Y i ∼ N(μ i, 1), i = 1, …,n, be independent random variables. We study the
problem of estimating the quantity S = Σ{i|C...
Về các Tập Hiệu (p2, p, p2, p) trong ℤ3 p Dịch bởi AI
Journal of Statistical Theory and Practice - Tập 6 - Trang 88-96 - 2012
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Recent developments in systematic sampling: A review
Journal of Statistical Theory and Practice - Tập 12 - Trang 290-310 - 2018
Systematic sampling is one of the most prevalent sampling techniques. The
popularity of the systematic design is mainly due to its practicality. Compared
with simple random sampling, it is easier to draw a systematic sample,
especially when the selection of sample units is done in the field. In addition,
systematic sampling can provide more precise estimators than simple random
sampling when expli...
On the role of the prior in multiplicity adjustment
Journal of Statistical Theory and Practice - Tập 10 - Trang 263-290 - 2016
Multiplicity adjustment in Bayesian analysis is achieved through the use of a
prior distribution, for the probability that a variable is in the (unknown)
model in the context of model selection, or for the probability that a null
hypothesis is true in the context of multiple testing. However, it is not
obvious how the prior distribution brings about multiplicity adjustment. In 2010
Scott and Berge...
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