Results 241 to 250 of about 910,585 (293)
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Lasso regression in sparse linear model with $$\varphi $$-mixing errors
Metrika, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Peng, Ling, Zhu, Yan, Zhong, Wenxuan
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Mixing partially linear regression models
Sankhya A, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liu, Song, Yang, Yuhong
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Double Penalized Quantile Regression for the Linear Mixed Effects Model
Journal of Systems Science and Complexity, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Hanfang, Liu, Yuan, Luo, Youxi
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Non‐linear mixed regression models
Environmetrics, 1995AbstractIn this paper we present an estimating equation approach to statistical inference for non‐linear random effects regression models for correlated data. With this approach, the distribution of the observations and the random effects need not be specified; only their expectation and covariance structure are required. The variance of the data given
Richard T. Burnett +2 more
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Bridge Estimation for Linear Regression Models with Mixing Properties
Australian & New Zealand Journal of Statistics, 2014SummaryPenalized regression methods have for quite some time been a popular choice for addressing challenges in high dimensional data analysis. Despite their popularity, their application to time series data has been limited. This paper concerns bridge penalized methods in a linear regression time series model.
Lee, Taewook +2 more
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Model checking for multiplicative linear regression models with mixed estimators
Statistica Neerlandica, 2021In this paper, we introduce the mixed estimators based on product least relative error estimation and least squares estimation in a multiplicative linear regression model. The asymptotic properties for the mixed estimators are established. We present some explicit expressions of the optimal estimator of the mixed estimators, and we also suggest some ...
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Linear mixed function‐on‐function regression models
Biometrics, 2014SummaryWe develop a linear mixed regression model where both the response and the predictor are functions. Model parameters are estimated by maximizing the log likelihood via the ECME algorithm. The estimated variance parameters or covariance matrices are shown to be positive or positive definite at each iteration.
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LOG‐LINEAR MODELS FOR MEAN AND DISPERSION IN MIXED POISSON REGRESSION MODELS
Australian Journal of Statistics, 1995SummaryThis paper is concerned with the analysis of repeated measures count data overdispersed relative to a Poisson distribution, with the overdispersion possibly heterogeneous. To accommodate the overdispersion, the Poisson random variable is compounded with a gamma random variable, and both the mean of the Poisson and the variance of the gamma are ...
Van de Ven, R., Weber, N. C.
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Bayesian cusp regression and linear mixed model
2023First of all, we introduce the Bayesian mixture way of solving the Cusp Catastrophe model, which is designed to deal with piece-wise continuous outcomes. Simulation and real data analysis show that the new method beats the old stochastic different equation solution of Cusp Catastrophe model.
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