Results 221 to 230 of about 7,479,809 (288)

Homophily‐adjusted social influence estimation

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Homophily and social influence are two key concepts of social network analysis. Distinguishing between these phenomena is difficult, and approaches to disambiguate the two have been primarily limited to longitudinal data analyses. In this study, we provide sufficient conditions for valid estimation of social influence through cross‐sectional ...
Hanh T.D. Pham, Daniel K. Sewell
wiley   +1 more source

Large parameter asymptotic analysis for homogeneous normalized random measures with independent increments

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Homogeneous normalized random measures with independent increments represent a broad class of Bayesian nonparametric priors and thus are widely used. In this article, we obtain the strong law of large numbers, the central limit theorem (CLT), and the functional central limit theorem (fCLT) of such measures when the concentration parameter a ...
Junxi Zhang, Shui Feng, Yaozhong Hu
wiley   +1 more source

A conversation with James V. Zidek

open access: yesCanadian Journal of Statistics, EarlyView.
AbstractThis article documents a series of exchanges between the authors and the senior Canadian statistician Jim Zidek in early 2026. The interview traces his life trajectory, surveying his principal contributions to statistics while offering insights into his motivations, successes, and challenges. Zidek is a Fellow of the Royal Society of Canada and
Christian Genest, Nancy E. Heckman
wiley   +1 more source

Revisiting Fisher's n‐D statistical vision: From algebraic abstraction to modern visualization

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract We revisit early foundational results in mathematical statistics derived by Ronald A. Fisher. They involve sampling distributions of statistics calculated from independent and identically distributed Normal observations, namely the root mean square deviation; the mean absolute deviation, conditional on already knowing the value of the root ...
James A. Hanley
wiley   +1 more source

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