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Statistical Inference in Linear Models
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ArtikelUtgivningsinformation: MDPI - Multidisciplinary Digital Publishing Institute 2024Beskrivning: 1 electronic resource (222 p.)Innehållstyp: - text
- computer
- online resource
- 9783725802579
- 9783725802586
- Computing and Information Technology
- Computer science
- Bayesian inference
- COVID-19
- Cauchy
- Farlie Gumbel Morgenstern (FGM) copula
- GARCH
- Kalman filter
- Monte Carlo simulation
- R package
- T-X transformation
- VAR
- Weibull distribution
- accelerated hazard model
- aggregate claims distribution
- biomedical
- bivariate
- bounded distribution
- censored data
- compound CMP regression model
- computation
- correlation
- crossover survival curves
- data sciences
- entropy
- estimation methods
- exponential-logarithmic distribution
- generalized half-logistic distribution (GHLD)
- generalized linear models
- generalized log-logistic distribution
- hazard function
- hazard-based regression model
- household financial affordability
- likelihood ratio test
- maximum likelihood estimation
- maximum likelihood estimators
- median rankit
- modal regression
- moments
- negative binomial distribution
- population mean
- prediction intervals
- pseudo-Poisson
- quantile
- quantile function
- quantile regression
- regression
- reliability parameter
- simulation
- sine function
- skewed data
- solar irradiation
- statistical properties
- sum of negative binomial variabl
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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.
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