2026 October - 26 articles
An overall missingness rate does not determine how much precision survives: at 25% missing, an exact calculation raises the standard error by 1.2% under one admissible mechanism and 21.4% under another. For variable or all-or-none block missingness a...
Selective prediction requires a statistically reliable rule for deciding which model outputs can be returned while controlling the error rate among accepted predictions. In large language model (LLM) applications, uncertainty scores provide useful ra...
The original Canonical Dual-STATIS biplot incorporates an a priori group structure into the analysis of a sequence of data tables, but its formulation requires the same individuals to be observed on every occasion, which excludes repeated cross-secti...
A collection of interrelated social variables determines crime deprivation at the neighborhood level in England. In penalized regression, strong intercorrelations pose a fundamental problem for variable selection. Previous research has used Elastic-N...
In the application of Bayesian methods, the choice of a prior density for defined model parameters is often a practical issue. In scientific and medical applications, it requires careful justification. In many settings, the properties of the statisti...
Sample size calculations return the smallest size that meets a requirement once the model and inputs are specified. We ask which sample sizes satisfy the design requirements when the planning information is compatible with several data distributions....
The two-parameter Birnbaum-Saunders distribution is widely applied in reliability engineering and fatigue life modeling due to its desirable statistical properties. In this paper, we propose improved maximum likelihood estimators for this distributio...
Continuous dequantization embeds discrete data into a continuous space, but relaxation can alter the statistical information carried by the original categories. We study a complementary regime in which a specified weighted discrete law is the target...
Background: Composite well-being indicators are typically built by aggregating dimension scores with subjectively elicited or transferred weights, computed on raw sample means without formal reliability checks. Methods: We propose the Reliability-Wei...
The beta autoregressive moving average (
Stats (ISSN 2571-905X) [...]
Reproducibility is a fundamental component of reliability in scientific research, reflecting the extent to which statistical conclusions remain stable under repeated experimentation. This paper investigates the reproducibility probability (RP) of Tuk...
Survival analysis plays a central role in medical research. Although the Cox proportional hazards (CoxPH) model remains the standard approach, machine learning and deep learning methods have been increasingly adopted. However, many published comparis...
Supervised learning in fields such as genomics and medical imaging is often hindered by the high cost of expert data annotation. Active learning addresses this bottleneck by iteratively selecting the most informative unlabeled samples for labeling. H...
Background. Human populations are exposed to complex chemical mixtures, making interaction detection a central challenge in environmental epidemiology. We benchmarked methods for prediction and recovery of interaction structure. Methods. Eight approa...
Non-inferiority (NI) clinical trials have gained immense popularity within the last decades, especially in cancer and cardiovascular studies. These trials are designed to establish the non-inferiority of a new experimental treatment as compared to th...
The authors wish to make the following correction to the original publication [...]
Subjective wellbeing is multidimensional, and satisfaction with financial circumstances, personal relationships, and time use may be interrelated while displaying different temporal, sociodemographic, and contextual patterns. Comparative evidence exa...
This study proposes a flexible three-parameter distribution to capture complex patterns in continuous positive data. By combining the generalized log-logistic odd generator with an error function distribution, our model successfully accommodates vari...
Sparse count data, such as patent document–keyword matrices, often contain excessive zeros and overdispersion, making conventional distance-based clustering methods less suitable. This study proposes a zero-inflated likelihood-based graph neura...
Background: The proliferation of fitness apps and wearable technology provides researchers interested in health and fitness with vast opportunities to test hypotheses involving a continuous stream of data collected on individual research participants...
The author wishes to make the following corrections to the original publication [...]
Rule-defined rankings often transform continuous marks, discrete judgments, trimming rules, caps, truncation, and tie-breaking variables into a single official order. When ranking margins are small, a formally valid outcome may nevertheless be sensit...
Sample sizes might be small in a variety of applications. Many classical and often-applied statistical methods rely on asymptotic approximations and, therefore, should not be used in the case of small sample sizes. Approaches that can be applied to s...
Distinguishingstructured nonlinear temporal behaviour from stochastic variability remains a fundamental challenge in the analysis of noisy and nonstationary time series, particularly when conventional nonlinear methods are sensitive to noise and fini...
Increasing the accuracy of age-at-death estimation using two skeletal-characteristic stages can enhance confidence in biological identification using forensic science. Transition analysis and the inverse prediction method with a Bayesian approach was...
