Results 221 to 230 of about 10,792 (301)

Model‐Free Local Partial Correlation

open access: yesAustralian &New Zealand Journal of Statistics, Volume 68, Issue 1, March 2026.
ABSTRACT In the simple linear regression context, partial correlation measures the linear association between two variables, with the linear effects of a third control variable removed. In this paper, we investigate the local partial correlation using a kernel smoothing approach.
Li‐Shan Huang   +2 more
wiley   +1 more source

Estimation of Daily Smoking Prevalence for Disaggregated Statistical Areas in Australia

open access: yesAustralian &New Zealand Journal of Statistics, Volume 68, Issue 1, March 2026.
ABSTRACT Motivated by the need to estimate prevalence at multiple disaggregated level hierarchies, rather than only one, this study extends widely used area‐level models in Bayesian and frequentist framework. We propose adding additional unstructured random effects at higher level disaggregated domains to the traditional models. Using our extension, we
Sumonkanti Das   +4 more
wiley   +1 more source

Words and Meters: Neural Evidence for a Connection Between Individual Differences in Statistical Learning and Rhythmic Ability in Infancy

open access: yesDevelopmental Science, Volume 29, Issue 2, March 2026.
ABSTRACT Music and language are both hierarchically structured: syllables combine into words, and meters are groupings of musical beats. Statistical learning (SL) supports speech segmentation through computation of transitional probabilities between syllables, and individual differences in SL ability were found predictive of further language ...
Iris van der Wulp   +2 more
wiley   +1 more source

The posterior estimates of the Cauchy error distribution.

open access: green
Mehmet Ali Cengiz (17536646)   +2 more
openalex   +1 more source

A Mixture Transition Distribution Modeling for Higher‐Order Circular Markov Processes

open access: yesJournal of Time Series Analysis, Volume 47, Issue 2, Page 304-320, March 2026.
ABSTRACT This study considers the stationary higher‐order Markov process for circular data by employing the mixture transition distribution modeling. The underlying circular transition distribution is based on Wehrly and Johnson's bivariate joint circular models.
Hiroaki Ogata, Takayuki Shiohama
wiley   +1 more source

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