Statistical Inference in Linear Models
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...
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| Médium: | Online |
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| Jazyk: | angličtina |
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MDPI - Multidisciplinary Digital Publishing Institute
2024
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| On-line přístup: | ONIX_20240514_9783725802579_196 |
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| _version_ | 1869519992270094336 |
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| collection | Directory of Open Access Books |
| description | 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 | Online |
| id | doab-20.500.12854ir-137596 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1375962024-05-14T13:41:18Z Statistical Inference in Linear Models Ferreira, Sandra 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 COVID-19 bounded distribution estimation methods Cauchy regression bivariate correlation likelihood ratio test maximum likelihood estimators pseudo-Poisson 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 hazard function solar irradiation quantile quantile function median rankit population mean thema EDItEUR::U Computing and Information Technology::UY Computer science 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. 2024-05-14T13:41:14Z 2024-05-14T13:41:14Z 2024 book ONIX_20240514_9783725802579_196 9783725802579 9783725802586 https://directory.doabooks.org/handle/20.500.12854/137596 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/8782 https://mdpi.com/books/pdfview/book/8782 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0258-6 10.3390/books978-3-7258-0258-6 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725802579 9783725802586 222 open access |
| spellingShingle | 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 COVID-19 bounded distribution estimation methods Cauchy regression bivariate correlation likelihood ratio test maximum likelihood estimators pseudo-Poisson 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 hazard function solar irradiation quantile quantile function median rankit population mean thema EDItEUR::U Computing and Information Technology::UY Computer science Statistical Inference in Linear Models |
| title | Statistical Inference in Linear Models |
| title_full | Statistical Inference in Linear Models |
| title_fullStr | Statistical Inference in Linear Models |
| title_full_unstemmed | Statistical Inference in Linear Models |
| title_short | Statistical Inference in Linear Models |
| title_sort | statistical inference in linear models |
| topic | 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 COVID-19 bounded distribution estimation methods Cauchy regression bivariate correlation likelihood ratio test maximum likelihood estimators pseudo-Poisson 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 hazard function solar irradiation quantile quantile function median rankit population mean thema EDItEUR::U Computing and Information Technology::UY Computer science |
| topic_facet | 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 COVID-19 bounded distribution estimation methods Cauchy regression bivariate correlation likelihood ratio test maximum likelihood estimators pseudo-Poisson 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 hazard function solar irradiation quantile quantile function median rankit population mean thema EDItEUR::U Computing and Information Technology::UY Computer science |
| url | ONIX_20240514_9783725802579_196 |