Results 311 to 320 of about 1,409,985 (359)

Bridge Estimation for Linear Regression Models with Mixing Properties

Australian & New Zealand Journal of Statistics, 2014
SummaryPenalized 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, Park, Cheolwoo, Yoon, Young Joo   +2 more
openaire   +1 more source

Model checking for multiplicative linear regression models with mixed estimators

Statistica Neerlandica, 2021
In 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 ...
openaire   +1 more source

LOG‐LINEAR MODELS FOR MEAN AND DISPERSION IN MIXED POISSON REGRESSION MODELS

Australian Journal of Statistics, 1995
SummaryThis 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.
openaire   +1 more source

Predicting PM2.5 in Well-Mixed Indoor Air for a Large Office Building Using Regression and Artificial Neural Network Models.

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, Shuoqi Wang, T. Larson, Amy A. Kim   +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, 2008
Classic 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, Chris Jermaine, Sanjay Ranka, John Gums   +3 more
openaire   +1 more source

Bayesian quantile regression for skew-normal linear mixed models

Communications in Statistics - Theory and Methods, 2016
AbstractLinear 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
openaire   +1 more source

Mixed data sampling (MIDAS) regression models

, 2020
Mixed data sampling (MIDAS) regressions are now commonly used to deal with time series data sampled at different frequencies. This chapter focuses on single-equation MIDAS regression models involving stationary processes with the dependent variable ...
Eric Ghysels, Virmantas Kvedaras, Vaidotas Zemlys-Balevičius   +2 more
semanticscholar   +1 more source

Rényi statistics for testing hypotheses in mixed linear regression models

Journal of Statistical Planning and Inference, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Molina, I., Morales, D.
openaire   +2 more sources

A new stochastic mixed ridge estimator in linear regression model

Statistical Papers, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Yalian, Yang, Hu
openaire   +2 more sources

Generalized linear mixed model for monitoring autocorrelated logistic regression profiles

The International Journal of Advanced Manufacturing Technology, 2012
Profile monitoring is used to monitor the regression relationship between a response variable and one or more explanatory variables over time. Many researches have been done in this area, but in most of them, the distribution of the response variable is assumed to be normal. However, this assumption is violated in many real case problems.
Mehdi Koosha, Amirhossein Amiri
openaire   +1 more source