Showing papers in "Journal of Statistical Planning and Inference in 2012"

TL;DR: In this article, the authors proposed zero-inflated Poisson and negative binomial INGARCH models, which are useful and flexible generalizations of the Poisson model, respectively.

TL;DR: A novel extension of Latin hypercube sampling (LHSMDU) for multivariate models is developed here that increases the multidimensional uniformity of the input parameters through sequential realization elimination.

TL;DR: For a random sample of size n obtained from a p-variate normal population, the likelihood ratio test (LRT) for the covariance matrix equal to a given matrix is considered in this article.

TL;DR: In this paper, a new method for sampling from a finite population that is spread in one, two or more dimensions is presented, where weights are used to create strong negative correlations between the inclusion indicators of nearby units.

TL;DR: In this paper, the authors studied the asymptotic properties of the Bernstein polynomial estimator and obtained explicit expressions for the bias and variance of the estimator.

TL;DR: This work characterize the optimal configurations of n points on the unit sphere for a class of potential functions in terms of their approximation properties within frame theory by means of probabilistic frames and indicates some connections between statistical shape analysis and frame theory.

TL;DR: In this paper, the first two moments of the truncated multivariate t (TMVT) distribution under the double truncation are computed and the results can be directly computed in existing software.

TL;DR: A very general and flexible model-based technique is used to cluster longitudinal data, usingixtures of multivariate t-distributions with a linear model for the mean and a modified Cholesky-decomposed covariance structure.

TL;DR: In this paper, the authors used Gibbs sampling procedure to draw Markov Chain Monte Carlo (MCMC) samples and used it to compute the Bayes estimates and also to construct symmetric credible intervals.

TL;DR: In this paper, an approach based on the principle of fiducial inference is presented for constructing prediction intervals for any given distribution. But the approach is limited to the normal, binomial, exponential, gamma and Weibull distributions.

TL;DR: In this paper, the authors examined the boundary properties of Bernstein estimators of density and distribution functions and showed that these estimators experience an advantageous boundary effect, and that they have decreased bias and variance in the boundary region.

TL;DR: In this paper, a rank-based nonparametric estimator of the Pickands dependence function is proposed, which is a function on the unit simplex subject to certain shape constraints that arise from an integral transform of an underlying measure called spectral measure.

TL;DR: In this paper, a general bootstrap method for hypothesis testing is studied, which preserves the data structure of each group independently and the null hypothesis is only used in order to compute the bootstrap statistic values (not at the resampling, as usual).

TL;DR: A mixture of latent variables model is proposed for the model-based clustering, classification, and discriminant analysis of data comprising variables with mixed type, a generalization of latent variable analysis.

TL;DR: In this paper, the authors proposed five ridge regression estimators for probit regression models for estimating the parameters when the weighted design matrix is ill-conditioned and it is suspected that the parameter β may belong to a linear subspace defined by H β = h.

TL;DR: In this paper, the authors introduce new statistical criteria for estimation, suitable for inference in models with common continuous support, which is in the direct line of a renewed interest for divergence based inference tools imbedding the most classical ones such as maximum likelihood, Chi-square or Kullback-Leibler.

TL;DR: In this paper, divergence minimization for models satisfying linear constraints with unknown parameter is introduced, and the problem of existence and characterization of the divergence projections of probability distributions on sets of signed finite measures is treated.

TL;DR: In this paper, the idea of step-up and step-down methods for multiple comparisons to sequential designs was extended for testing multiple hypotheses, resulting in a statistical decision for each individual test and controlling the family-wise error rate and the familywise power.

TL;DR: In this article, the performance of the UMVUE and the MLE estimators were compared numerically based on extensive Monte Carlo simulation, and it was shown that the performance was about the same.

TL;DR: In this paper, bias adjustments to the point estimator proposed by Schouten et al. (2009) were proposed and demonstrated in a simulation study where it is shown that the method is valid, especially for smaller sample sizes.

TL;DR: This paper forms a test of isotropy for spatial observations located according to a general class of stochastic designs and applies it to a data set on longleaf pine trees from an oldgrowth forest in the southern United States.

TL;DR: In this article, the authors investigated the asymptotic behaviour of a studentized permutation test for testing equality of Pearson correlation coefficients in two populations and showed that this test is asymPTotically of exact level and has the same power for contiguous alternatives as the corresponding asymptonotic test.

TL;DR: In this article, the authors compare the performance of the method of variance estimates recovery (MOVER) and the generalized confidence interval procedure to coverage of credibility intervals obtained using Bayesian methodology using a variety of different prior distributions to estimate the appropriateness of each.

TL;DR: In this article, the authors proposed a cure rate survival model by assuming the number of competing causes of the event of interest follows the Geometric distribution and the time to event follow a Birnbaum Saunders distribution.

TL;DR: In this article, the authors explore the problem of testing the hypothesis that the covariance matrix is an identity matrix when the dimensionality is equal to the sample size or larger, and propose two new test statistics under comparable assumptions to those statistics in the literature.

TL;DR: In this article, the authors consider linear vector autoregressive (VAR) models where the innovations could be unconditionally heteroscedastic and derive the asymptotic distribution of the proposed estimators for the VAR coefficients and compare their properties.

TL;DR: In this article, a new class of models to fit longitudinal data, obtained with a suitable modification of the classical linear mixed-effects model, is presented, where the joint distribution of the random effect and the random error is a finite mixture of scale mixtures of multivariate skew-normal distributions.

TL;DR: In this paper, the authors consider semiparametric additive regression models with a linear parametric part and a nonparametric part, both involving multivariate covariates, and prove a functional central limit theorem for the residual-based empirical distribution function, up to a uniformly negligible remainder term.

TL;DR: In this article, the authors proposed a simple and accurate approximate degrees of freedom (ADF) test, which is invariant under affine transformations, different choices of contrast matrix for the same null hypothesis, or different labeling schemes of cell means.

TL;DR: Numerical results show that there are versions of adaptive Bonferroni and S˘idak methods that can perform better under certain positive dependence situations than those previously considered and are numerically seen to often outperform the previously considered adaptive Holm method.