Results 31 to 40 of about 167,561,462 (173)
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract Generalizability theory (G‐theory) defines a statistical framework for assessing measurement reliability by decomposing observed variance into meaningful components attributable to persons, facets, and error. Classic G‐theory assumes homoscedastic residual variances across measurement conditions, an assumption that is often violated in ...
Philippe Rast, Peter E. Clayson
wiley +1 more source
Nonparametric Predictive Inference for Multiple Comparisons [PDF]
, 2010 This thesis presents Nonparametric Predictive Inference (NPI) for several multiple comparisons problems. We introduce NPI for comparison of multiple groups of data including right-censored observations.
Maturi, T.A., MATURI, TAHANI
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ReMoDe – Recursive modality detection in distributions of ordinal data
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract The detection of the number of modes in distributions of ordinal data is relevant for applied researchers across disciplines, from uncovering polarization to detecting incidence groups in clinical symptom scales. Yet, established modality detection methods are either purely descriptive or not developed for ordinal data.
Madlen Hoffstadt +3 more
wiley +1 more source
Asymptotic properties of the Bernstein density copula for dependent data [PDF]
Copulas are extensively used for dependence modeling. In many cases the data does not reveal how the dependence can be modeled using a particular parametric copula. Nonparametric copulas do not share this problem since they are entirely data based.
ROMBOUTS, Jeroen V.K. +2 more
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Using multilabel classification neural network to detect intersectional DIF with small sample sizes
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract This study introduces InterDIFNet, a multilabel classification neural network for detecting intersectional differential item functioning (DIF) in educational and psychological assessments, with a focus on small sample sizes. Unlike traditional marginal DIF methods, which often fail to capture the effects of intersecting identities and require ...
Yale Quan, Chun Wang
wiley +1 more source
Bootstrap tests for simple structures in nonparametric time series regression. [PDF]
This paper concerns statistical tests for simple structures such as parametric models, lower order models and additivity in a general nonparametric autoregression setting.
Yao, Qiwei +2 more
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Nonparametric and semiparametric methods for economic research [PDF]
, 1992 Developments in the vast and growing literatures on nonparametric and semiparametric statistical estimation are reviewed. The emphasis is on useful methodology rather than statistical properties for their own sake. Some empirical applications to economic
Robinson, Peter M., Delgado, Miguel A.
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Asymptotic standard errors for reliability coefficients in item response theory
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract In a recent review, Liu et al. (Psychological Methods, 2025b) classified reliability coefficients into two types: classical test theory (CTT) reliability and proportional reduction in mean squared error (PRMSE). This article focuses on quantifying the sampling variability of these coefficients under item response theory (IRT) models.
Youjin Sung, Yang Liu
wiley +1 more source
New estimation methods for diagnostic classification models
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract This paper introduces a new class of estimators for cognitive diagnosis models (CDMs) based on the Cressie–Read family of ϕ$$ \phi $$‐divergences. Focusing on the loglinear CDM (LCDM), for which joint maximum likelihood estimation (JMLE) has been shown to be consistent, we propose a joint minimum divergence estimation (JMDE) framework.
Elena Castilla
wiley +1 more source
British Journal of Mathematical and Statistical Psychology, EarlyView.Abstract Cognitive diagnostic models (CDMs) have become essential tools for providing fine‐grained information about individuals' mastery of cognitive skills. While prior reviews have emphasized statistical foundations and deep learning‐based developments, this article focuses on recent methodological innovations designed to address persistent ...
Chun Wang, Yale Quan, David Arthur
wiley +1 more source