Showing papers in "Journal of Statistical Planning and Inference in 2023"

TL;DR: In this paper , the authors used the normalized version of PS×(1-PS) with PS denoting the propensity score to estimate the overlap weight (OW), i.e., when subjects in one group have the best overlap with the other group.

TL;DR: In this article , a data-driven procedure that aims to fully utilize the clustering information is proposed, which integrates the kernel-based aggregation of spatial observations with a global loss function at the temporal level to group data into several sets, and derive an FDR control scheme for set-wise multiple testing.

TL;DR: In this article , the authors show that the FOU(p) process can be used to model a wide range of time series varying from short range dependence to long range dependence, with performance similar to ARMA or ARFIMA models and in several cases outperforming them.

TL;DR: In this article , the authors studied the problem of maximizing the probability that a random variable X is not smaller than another random object Y and that X and Y coincide within the class of all random variables X, Y with given univariate continuous distribution functions F and G , respectively.

TL;DR: In this paper , a new characterization of the collection of all probability distributions for (X,X) is proved and a certain subclass of Λ, defined in terms of copulas, is introduced.

TL;DR: In this paper , partial areas under ROC curve segments are used to represent clinically relevant biomarker cut-off values, and a nonparametric multiple contrast test for these parameters is constructed and shown to asymptotically control the family-wise type one error rate.

TL;DR: In this paper , the authors proposed a method to sample from exchangeable multivariate Bernoulli distributions and determine the distributions and the bounds of a wide class of indices and measures of probability mass functions.

TL;DR: In this article , a statistical model for image classification, namely a hierarchical max-pooling model with additional local pooling, is introduced, which enables the hierarchical model to combine parts of the image which have a variable relative distance towards each other.

TL;DR: Wang et al. as discussed by the authors proposed a sure independence screening procedure based on weighted sum of squared conditional correlations (WSSCC), which measures the correlation between a random variable and its function.

TL;DR: In this article , a method-of-moments estimator, given by a quadratic form of a symmetric matrix A, is used as a starting point and modified using the class of square non-singular regularization matrices Q while preserving unbiasedness in addition to ensuring positivity.

TL;DR: In this paper , the authors investigate statistical inference in partially linear single-index quantile regression with high dimensional linear and single index parameters when the observations are missing at random, which allows the response or covariates or response and covariates simultaneously missing.

TL;DR: In this article , the adequacy test of the partial functional linear model when the scalar predictors are measured with additive errors is studied. But the authors focus on the U-statistic-based test, which can control type I error well, and its power performance is satisfactory.

TL;DR: Ganesh and O'Connell as mentioned in this paper proved the large deviation principle (LDP) for posterior distributions arising from subfamilies of full exponential families, allowing misspecification of the model.

TL;DR: In this paper , the authors proposed a numerical method for obtaining exact confidence intervals of likelihood-based parameter estimators for general multi-parameter models without loss of accuracy, which is in sharp contrast to other multiparameter extensions of the test inversion.

TL;DR: In this paper , the authors studied the model in a more realistic setting where the excitation kernel also depends on the limit index n, so that the waiting times to excited events are of the same order of magnitude as those between baseline events.

TL;DR: In this article , the authors developed covariate-adjusted response-adaptive designs for censored Weibull responses, which are based on covariate adjusted doubly-adaptively biased coin design and the covariateadjusted efficient randomized adaptive design, and the treatment allocation proportion converges empirically to the expected target value.

TL;DR: In this paper , a rank-based likelihood was proposed for the non-paranormal graphical model using a ranklikelihood which remains invariant under monotone transformations, thereby avoiding the need to put a prior on the transformation functions.

TL;DR: In this paper , an adaptive design that incorporates observed Fisher information is proposed, which is shown to be more efficient than the optimal design with respect to inference, and the proposed design is used to develop a sample size calculation.

TL;DR: In this article , the authors present an alternative and more direct proof of the result for generalized Bayes estimators corresponding to priors which are a subclass of scale mixtures of spherical normals.

TL;DR: In this paper , the generalized Bayes estimators of the covariance matrix are given with closed forms, and the dominance properties for the Stein loss function are discussed for both matrix and scalar quadratic loss functions.

TL;DR: The reach of a set M⊂Rd, also known as condition number when M is a manifold, was introduced by Federer in 1959 as mentioned in this paper , and is a central concept in geometric measure theory, set estimation, manifold learning, among others areas.

TL;DR: In this article , a pseudo-partial likelihood estimation method for estimating parameters in the Cox proportional hazards model with right-censored and biased sampling data by adjusting sample risk sets is proposed.

TL;DR: In this paper , the rate of convergence of least squares estimates based on fully connected spaces of deep neural networks with ReLU activation function is analyzed for smooth regression functions, and it is shown that under an manifold support assumption, DNNs circumvent the curse of dimensionality.

TL;DR: In this article , a penalized empirical likelihood (EL) method based on robust estimating functions is proposed for regularizing the regression parameters and the associated Lagrange multipliers simultaneously, which allows the dimensionality of both regression parameter and estimating equation to grow exponentially with the sample size.

TL;DR: In this paper , the authors address the issue of heterogeneity and how to obtain rigorous, reliable and reproducible evaluation of different dose-finding designs, i.e., they make comparisons before they average rather than the usual practice which is the other way around.