Reprint
Statistical Inference in Linear Models
Edited by
February 2024
222 pages
- ISBN978-3-7258-0257-9 (Hardback)
- ISBN978-3-7258-0258-6 (PDF)
This is a Reprint of the Special Issue Statistical Inference in Linear Models that was published in
Computer Science & Mathematics
Summary
Linear models are statistical models that play a crucial role in several fields of science and are of practical importance in statistics. The most typical type is the linear regression model. Many phenomena, such as those in biology, medicine, economics, management, geology, meteorology, agriculture and industry, can be approximately described with linear models. Thus, the further research and development of linear models is still a hot research topic.
Format
- Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
exponential-logarithmic distribution; T-X transformation; moments; entropy; maximum likelihood estimation; simulation; data sciences; Farlie Gumbel Morgenstern (FGM) copula; generalized half-logistic distribution (GHLD); reliability parameter; Monte Carlo simulation; statistical properties; household financial affordability; Bayesian inference; hazard-based regression model; survival analysis; accelerated hazard model; generalized log-logistic distribution; crossover survival curves; censored data; maximum likelihood estimation; COVID-19; bounded distribution; estimation methods; Cauchy; regression; bivariate; correlation; likelihood ratio test; maximum likelihood estimators; pseudo-Poisson; regression; Kalman filter; VAR; GARCH; quantile regression; modal regression; biomedical; unit distribution; skewed data; aggregate claims distribution; compound CMP regression model; generalized linear models; prediction intervals; negative binomial distribution; computation; R package; sum of negative binomial variables; sine function; Weibull distribution; moments; estimation methods; hazard function; solar irradiation; quantile; quantile function; median rankit; population mean