Results 231 to 240 of about 107,786 (317)

Fluctuation-dissipation theorem and radiative heat transfer in nonlinear Kerr media

open access: green, 2014
Chinmay Khandekar   +3 more
openalex   +1 more source

Rank‐based estimation of propensity score weights via subclassification

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Propensity score (PS) weighting estimators are widely used for causal effect estimation and enjoy desirable theoretical properties, such as consistency and potential efficiency under correct model specification. However, their performance can degrade in practice due to sensitivity to PS model misspecification.
Linbo Wang   +3 more
wiley   +1 more source

Predicting cervical cancer DNA methylation from genetic data using multivariate CMMP

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Epigenetic modifications link the environment to gene expression and play a crucial role in tumour development. DNA methylation, in particular, is gaining attention in cancer research, including cervical cancer, the focus of this study.
Hang Zhang   +5 more
wiley   +1 more source

Experimentally separating vacuum fluctuations from source radiation. [PDF]

open access: yesNat Commun
Herter A   +4 more
europepmc   +1 more source

On subset least squares estimation and prediction in vector autoregressive models with exogenous variables

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract We establish the consistency and the asymptotic distribution of the least squares estimators of the coefficients of a subset vector autoregressive process with exogenous variables (VARX). Using a martingale central limit theorem, we derive the asymptotic normal distribution of the estimators. Diagnostic checking is discussed using kernel‐based
Pierre Duchesne   +2 more
wiley   +1 more source

Asymptotic properties of cross‐classified sampling designs

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract We investigate the family of cross‐classified sampling designs across an arbitrary number of dimensions. We introduce a variance decomposition that enables the derivation of general asymptotic properties for these designs and the development of straightforward and asymptotically unbiased variance estimators.
Jean Rubin, Guillaume Chauvet
wiley   +1 more source

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