Showing papers in "Journal of Statistical Planning and Inference in 2015"

TL;DR: A framework for comparing algorithm-generated Pareto fronts based on a refined hypervolume indicator is proposed and an empirical rule for the three-dimensional case is proposed by making an analogy to the rules for two dimensions.

TL;DR: In this paper, various Markov-switching autoregressive models for bivariate time series which describe wind conditions at a single location have been proposed, where the hidden Markov chain is not homogeneous, its evolution depending on past wind conditions.

TL;DR: In this paper, the authors investigated the asymptotic properties of the estimator for the regression function operator whenever the functional stationary ergodic data with missing at random (MAR) are considered.

TL;DR: This work gives general regularity conditions under which the asymptotic null behavior of the corresponding tests in addition to their behavior under alternatives are derived, where conditions become particularly simple for sufficiently smooth estimating and monitoring functions.

TL;DR: In this paper, the authors provide a unified framework for testing a null hypothesis whenever the test statistic has a discrete distribution and provide tools for plotting adjusted abstract randomized p-values and for computing multiple test functions.

TL;DR: In this paper, additive and nonnegative bias correction techniques, originally developed for the standard kernel estimator, are applied to some asymmetric kernel estimators when the underlying density has a fourth order derivative.

TL;DR: In this paper, the authors investigate the problem of constructing credible sets that are honest and adaptive for the L2L2-loss over a scale of Sobolev classes with regularity ranging between [D, 2D][D,2D], for some given DD in the context of the signal-in-white-noise model.

TL;DR: This is the first work that studies hypothesis testing, when data have such complex multilevel functional and spatial structure, and two small-sample alternatives, including a novel block bootstrap for functional data, are proposed.

TL;DR: In this article, the authors developed an efficient Bayesian method to cluster curve data using an elastic shape metric that is based on joint registration and comparison of shapes of curves, and the elastic-inner product matrix obtained from the data is modeled using a Wishart distribution whose parameters are assigned carefully chosen prior distributions to allow for automatic inference on the number of clusters.

TL;DR: In this article, a penalty on the range of the regression function is proposed to correct the spiking problem for univariate and multivariate isotonic models, and the penalized estimator is consistent everywhere for a wide range of sizes of the penalty parameter.

TL;DR: In this article, the authors considered a more general class of tests which form a superfamily of the procedures described by Basu et al. (2013a), and theoretically proved several robustness results of the new class of test and illustrate them in the normal model.

TL;DR: In this article, the estimation of a semi-parameter varying-coefficient spatial panel data model with random effects is studied and a root-N consistent estimator for the unknown parameter is proposed.

TL;DR: A Bayesian nonparametric procedure for density estimation for data in a d -dimensional simplex is proposed based on a modified class of multivariate Bernstein polynomials, with random weights and a random number of components.

TL;DR: In this paper, three difference-based methods are proposed for variance estimation under both balanced and unbalanced repeated measurement settings: the sample variance method, the partitioning method, and the sequencing method.

TL;DR: In this article, a generalized likelihood ratio test is proposed to perform curve registration. But, the test statistic is asymptotically distributed as a chi-squared random variable.

TL;DR: In this paper, the optimal design problem under second-order least squares estimation was studied under asymmetric error distribution and a general approximate theory was developed, taking cognizance of the nonlinearity of the underlying information matrix in the design measure.

TL;DR: In this article, two types of results that support the use of Generalized Cross Validation (GCV) for variable selection under the assumption of sparsity were investigated, based on the well established links between GCV on one hand and Mallows's C p and Stein Unbiased Risk Estimator (SURE).

TL;DR: In this paper, a new analytical expression of the centered L 2 -discrepancy measure of uniformity for mixed two and three-level U-type designs in depth was proposed.

TL;DR: In this article, a general hierarchical Bayesian framework that incorporates a flexible nonparametric data model specification through the use of empirical likelihood methodology, which they termed semiparametric hierarchical empirical likelihood (SHEL) models, is introduced.

TL;DR: In this paper, a Laplace deconvolution on R + in a white noise framework is considered, where the convolution kernel is unknown and accessible only through experimental noise, and the resulting process is adaptive with respect to the target function's smoothness, but not to the unknown degree of illposedness of the operator.

TL;DR: In this paper, a polynomial spline basis function expansion and a smoothly clipped absolute deviation penalty are applied to perform estimation and variable selection in the framework of a diverging number of index parameters.

TL;DR: This paper deals with the model selection problem in the context of partial linear models in which the regression function is assumed to be the additive form of the parametric component and the nonparametric component using Gaussian process priors, and Bayes factor consistency is investigated for choosing between theparametric model and the semiparametric alternative.

TL;DR: Under the model of genetic Brownian motion, it is proved that as the number of genetic sites that are sampled becomes large, the maximum likelihood estimator of the tree is consistent.

TL;DR: A robust mixture modeling approach is proposed using a mean-shift formulation coupled with nonconvex sparsity-inducing penalization, to conduct simultaneous outlier detection and robust parameter estimation, and an efficient iterative thresholding-embedded EM algorithm is developed to maximize the penalized log-likelihood.

TL;DR: In this paper, a modified Cholesky decomposition is proposed for estimating varying-coefficient models for longitudinal data, where the within-subject covariance matrix is decomposed into a unit triangular matrix involving generalized autoregressive coefficients and a diagonal matrix involving innovation variances.

TL;DR: The strong consistency of the estimates is shown for subgaussian random variables whose characteristic function vanishes nowhere, and this consistency result does not require any assumptions on the structure or the smoothness of the regression function.

TL;DR: In this article, a profile quasi-log-likelihood estimation method is applied with asymptotic consistency and normality established for the profile estimators, and Rao-score-type test procedures are developed based on the profile estimation for regression parameters and nonparametric coefficient functions, respectively.

TL;DR: In this article, the authors consider the nonparametric regression problem, where they apply Bayesian methods, taking scaled Brownian motion as a prior, and construct credible sets using the posterior distribution, which are then studied using frequentist methods.

TL;DR: In this paper, two weighted estimators are proposed to improve the estimation efficiency in quantile regression with longitudinal data, where the weights are quantile adaptive, which borrow information from the intra-subject correlation of the conditional quantile scores, rather than the conditional least squares scores.

TL;DR: In this paper, the problem of error control of stepwise multiple testing procedures is considered, and some new stepwise procedures are developed that control type 1 and directional errors under independence and various dependencies.