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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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Gespeichert in:
Format: Online
Sprache:Englisch
Veröffentlicht: MDPI - Multidisciplinary Digital Publishing Institute 2024
Schlagworte:
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
Online-Zugang:ONIX_20240514_9783725802579_196
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ONIX_20240514_9783725802579_196

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