Showing papers in "Journal of Statistical Planning and Inference in 2016"

TL;DR: In this paper, the authors provide a general theory whereby one can construct a permutation test of a parameter θ (P, Q ) = θ 0 which controls the asymptotic probability of the Type 1 error in large samples while retaining the exactness property when the underlying distributions are identical.

TL;DR: This work develops a coherent methodology for the constructuion of bootstrap prediction intervals for time series that can be modeled as linear, nonlinear or nonparametric autoregressions, and presents detailed algorithms for these different models.

TL;DR: A data-driven version of the TLTA is offered by estimating the parameters using EM algorithms and providing simulation evidence of its favorable performance relative to these competitors, showing its superior performance over its competitors that ignore the group structure.

TL;DR: This method is an extension of the adaptive lasso with nonnegative constraint on the coefficients and it is shown to have asymptotic unbiasedness, asymPTotic normality and variable selection consistency and its mean squared error decays fast too.

TL;DR: In this article, the authors exploit a max-mixture model to suggest a general spatial model which ensures extremal dependence at small distances, possible independence at large distances and asymptotic independence at intermediate distances.

TL;DR: In this paper, a particular variation on the IM construction is considered, one based on generalized associations, which is more flexible in that it does not require a complete specification of the data-generating process and is provably valid under mild conditions.

TL;DR: A modified version of the Schwarz type criterion that applies a harsher penalty equivalent to the model with one additional unknown parameter per segment is considered and it is empirically observed that the asymptotic behavior is comparable to that of the criterion proposed by Liu, Wu, and Zidek (1997).

TL;DR: In this paper, the authors proposed a simultaneous model structure identification and variable selection method, which can do simultaneous coefficient estimation and three kinds of selections (constant or varying effects and relevant variables).

TL;DR: In this paper, an inverse probability of censoring-weighted kernel version of Hill's estimator of the extreme value index is proposed and its asymptotic normality is established.

TL;DR: In this article, the problem of using prior information supplied by the model, in conjunction with prior beliefs about its parameters, to design the collection of data such that it is optimal for decisions which must be made using posterior beliefs about the model parameters is addressed.

TL;DR: In this article, pathwise invariances and degeneracies of random fields with motivating applications in Gaussian process modelling are studied, where a number of structural properties one may wish to impose a priori on functions boil down to degeneracy properties under well-chosen linear operators.

TL;DR: A modified multiple imputation framework is applied in the analysis of incomplete data in which values are missing for three or more qualitatively different reasons and a proof of the methodology used for three-stage multiple imputations with its limiting distribution is included.

TL;DR: A general method of construction of asymmetric orthogonal arrays was proposed by Suen et al. as mentioned in this paper, using this method and some modifications thereof, they constructed some more families of symmetric arrays of strength greater than two.

TL;DR: In this paper, the asymptotic validity of a resampling method for two sequential processes constructed from non-degenerate U -statistics is established under mixing conditions.

TL;DR: This work proves the consistency of the exact maximum likelihood estimators and uses the EM algorithm to compute it and defines a classification rule based on estimated posterior probabilities for N independent stochastic processes.

TL;DR: In this article, the authors point out some limitations of this test and propose some modifications to overcome them retaining its distribution-free property and demonstrate the utility of their proposed modifications on simulated and real data sets.

TL;DR: Optimal designs evaluated using ABCdE are compared to those evaluated using existing methods for the stochastic death and susceptible–infectious–susceptible (SI) models.

TL;DR: In this article, the impact of the bias in the invariant state space models with estimated parameters is discussed and an adaptive correction procedure based on any parameters estimation method (for instance, maximum likelihood or distribution-free estimators).

TL;DR: In this article, a nonparametric data-driven strategy was proposed to select a relevant projection space to estimate the cumulative distribution function of a known function ψ under the assumption that the distribution of Y is known and only Z is observed.

TL;DR: In this article, asymptotic properties of the first principal component in the HDLSS context and apply them to equality tests of covariance matrices for high-dimensional data sets are studied.

TL;DR: In this article, a multivariate semiparametric partial linear model for both linear and random design cases is considered, where the linear component is estimated based on the dierences of observations and the functional component is calculated using the multivariate Nadaraya-Watson kernel smoother of the residuals of the linear t. All of the proposed procedures are easy to implement.

TL;DR: In this paper, the authors proposed the Frechet distribution for the innovations of the autoregressive conditional duration (ACD) model, and hence, they derived some statistical inference tools including the maximum likelihood estimation and diagnostic tools for model adequacy, and their finite-sample performance is evaluated by Monte Carlo simulation experiments.

TL;DR: In this paper, the authors discuss another negative aspect of orbital tuning that has been ignored to this point, namely, orbital tuning contributes to a type of negative analytical bias against research aimed at modifying the Milankovitch theory.

TL;DR: In this article, the authors provide an extension to the methodology of bootstrap prediction intervals for linear ARX models, based on the assumption that the data series is Markov of order p.i.d.

TL;DR: Omnibus tests for central symmetry of a bivariate probability distribution are proposed in this article, where the test statistics compare empirical measures of opposite regions under rather weak conditions, and it follows that they are asymptotically distribution-free.

TL;DR: In this paper, Zhang and Mukerjee extended the GMC criterion to the B-GMC 2 n − m : 2 r design with 5 N / 16 + 1 ≤ n ≤ N / 2.

TL;DR: It is shown that the clustering structure is not informative on the existence of interactions in a consistent manner, allowing for a more efficient exploration of the log-linear model space.

TL;DR: In this article, Stein's method is used for proving central limit theorems along with explicit error bounds in probability theory, where uniform and non-uniform Berry-Esseen bounds spark general interest.

TL;DR: In this paper, under D - and D A -optimality criteria, the product of an optimal treatment allocation and an optimal design for covariates is also optimal among all possible designs for this linear additive model.

TL;DR: In this article, one-sided exact STI's have been constructed for a polynomial regression model over any given interval, and the available computer program allows the exact methods developed in this paper to be implemented easily.