Results 91 to 100 of about 8,530 (262)

Optimal model‐based design of experiments for parameter precision: Supercritical extraction case

open access: yesThe Canadian Journal of Chemical Engineering, EarlyView.
Abstract This study investigates the process of chamomile oil extraction from flowers. A parameter‐distributed model consisting of a set of partial differential equations is used to describe the governing mass transfer phenomena in a cylindrical packed bed with solid chamomile particles under supercritical conditions using carbon dioxide as a solvent ...
Oliwer Sliczniuk, Pekka Oinas
wiley   +1 more source

MLEce: Statistical inference for asymptotically efficient closed-form estimators in R

open access: yesSoftwareX
Maximum likelihood estimation is a classical method with useful properties like efficiency, consistency, and asymptotic normality. However, the maximum likelihood estimator (MLE) cannot be in closed form in many distributions.
Jun Zhao   +5 more
doaj   +1 more source

Carbon monoxide adsorption on NaY zeolite and impregnated with 5% niobium: Characteristics and fixed‐bed study

open access: yesThe Canadian Journal of Chemical Engineering, EarlyView.
Schematic of the CO adsorption process in a fixed‐bed column containing pure NaY zeolite and Nb‐modified NaY (NaY–5%Nb). On the left, breakthrough curves and temperature profiles highlight the dynamic performance and thermal effects during CO adsorption.
Elson Oliveira   +3 more
wiley   +1 more source

Asymptotic Properties of a Statistical Estimator of the Jeffreys Divergence: The Case of Discrete Distributions

open access: yesMathematics
We investigate the asymptotic properties of the plug-in estimator for the Jeffreys divergence, the symmetric variant of the Kullback–Leibler (KL) divergence. This study focuses specifically on the divergence between discrete distributions. Traditionally,
Vladimir Glinskiy   +5 more
doaj   +1 more source

Restricted Tweedie stochastic block models

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley   +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

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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