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Person-time generalized linear regression mixed models in survival analysis
StatisticsThis study presents a methodological contribution within a discrete-time survival analysis framework by introducing a person-time generalized linear mixed model. An application of this methodology, using logistic models with random effects, is provided in the context of academic career progression in Italy, a field where gender disparities remain a ...
Mezzetti, Maura, Negri, Ilia
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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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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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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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Environmental Science and Technology, 2020
Although the exposure to PM2.5 has serious health implications, indoor PM2.5 monitoring is not a widely applied practice. Regulations on the indoor PM2.5 level and measurement schemes are not well established.
Brent Lagesse +3 more
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Although the exposure to PM2.5 has serious health implications, indoor PM2.5 monitoring is not a widely applied practice. Regulations on the indoor PM2.5 level and measurement schemes are not well established.
Brent Lagesse +3 more
semanticscholar +1 more source
A bayesian mixture model with linear regression mixing proportions
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 2008Classic mixture models assume that the prevalence of the various mixture components is fixed and does not vary over time. This presents problems for applications where the goal is to learn how complex data distributions evolve. We develop models and Bayesian learning algorithms for inferring the temporal trends of the components in a mixture model as a
Xiuyao Song +3 more
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Bayesian quantile regression for skew-normal linear mixed models
Communications in Statistics - Theory and Methods, 2016AbstractLinear mixed models have been widely used to analyze repeated measures data which arise in many studies. In most applications, it is assumed that both the random effects and the within-subjects errors are normally distributed. This can be extremely restrictive, obscuring important features of within-and among-subject variations.
A. Aghamohammadi, M. R. Meshkani
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