All Research Papers Published In Journal of Statistical Planning and Inference

Research Article
10.1016/j.jspi.2026.106379

Max–min experimental designs for comparing pairs of treatments with binary outcomes

Jul 01, 2026
Journal of Statistical Planning and Inference
Satya Prakash Singh + 1 more +1

Research Article
10.1016/j.jspi.2026.106412

STRAW: Structure-Adaptive Weighting procedure for large-scale spatial multiple testing

Apr 01, 2026
Journal of Statistical Planning and Inference
Pengfei Wang + 2 more +2

Research Article
10.1016/j.jspi.2026.106420

Least squares and adaptive Lasso estimations of AR( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e3763" altimg="si11.svg"> <mml:mi>p</mml:mi> </mml:math> ) models with unit roots and heavy-tailed noises

Apr 01, 2026
Journal of Statistical Planning and Inference
Feifei Guo + 3 more +3

Research Article
10.1016/j.jspi.2025.106315

Bootstrap-based tests for the total time on test and the excess wealth orders

Mar 01, 2026
Journal of Statistical Planning and Inference
Tommaso Lando + 1 more +1

Given a pair of non-negative random variables X and Y , we introduce a class of nonparametric tests for the null hypothesis that X dominates Y in the total time on test order. Critical values are determined using bootstrap-based inference, and the tests are shown to be consistent. The same approach is used to construct tests for the excess wealth order. As a byproduct, we also obtain a class of goodness-of-fit tests for the NBUE (New Better than Used in Expectation) family of distributions. • Bootstrap tests for the TTT and excess wealth orders. • Bootstrap tests for the NBUE property • Asymptotic properties are established under mild conditions. • Simulations and application to real data.

Research Article
10.1016/s0378-3758(25)00082-5

Editorial Board

Mar 01, 2026
Journal of Statistical Planning and Inference

Research Article
10.1016/j.jspi.2025.106330

Sampling effort and its allocation in the Lincoln–Petersen experiment: A hierarchical approach

Mar 01, 2026
Journal of Statistical Planning and Inference
Su Na Chin + 2 more +2

Capture-recapture methods are widely used for estimating population sizes in ecological and epidemiological studies, yet the optimal allocation of sampling effort often remains underexplored. This study explores sampling efforts in a hierarchical framework that subdivides each capture occasion in a Lincoln-Petersen experiment into multiple sub-occasions, allowing for flexible resource allocation. When detection probabilities are equal across occasions, an even split minimises variance; when probabilities differ, maximizing the joint detection probability is essential. A pseudo-Bayesian approach is also proposed to address scenarios with unknown catchabilities. Detailed simulation studies validate the theoretical findings and demonstrate the framework’s robustness. The resulting guidelines offer practical insights for designing more efficient capture-recapture experiments and improving population size estimates.

Research Article
10.1016/s0378-3758(25)00058-8

Editorial Board

Jan 01, 2026
Journal of Statistical Planning and Inference

Research Article
10.1016/j.jspi.2025.106291

Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent

Dec 01, 2025
Journal of Statistical Planning and Inference
Michael Kohler + 2 more +2

Research Article
10.1016/j.jspi.2025.106275

Asymptotic normality and Cramér-type moderate deviations of Yule’s nonsense correlation statistic for Ornstein–Uhlenbeck processes

Sep 01, 2025
Journal of Statistical Planning and Inference
Jingying Zhou + 2 more +2

Research Article
10.1016/j.jspi.2024.106261

Marginally constrained nonparametric Bayesian inference through Gaussian processes

Jul 01, 2025
Journal of Statistical Planning and Inference
Bingjing Tang + 1 more +1