Incorporating gene annotation information into fine-mapping quantitative trait loci in genome-wide association studies: a hierarchical generalized linear model approach Authors: Yumin Xiao Supervisor: Xia Shen

Source URL: www.statistics.du.se

Statistical Learning, FallAssignment 2: Classification The purpose of this exercise is to compare the Naive Bayes (NB) model to Logistic Regression (LR) on data with categorical attributes. We will first train/fit

Source URL: www.timvanerven.nl

• Econometrics
• Actuarial science
• Statistical classification
• Naive Bayes classifier
• Training set
• Logistic regression
• Overfitting
• Generalized linear model
• Supervised learning
• Statistics
• Regression analysis
• Machine learning

Spatial-temporal association between fine particulate matter and daily mortality 1 Jungsoon Choi, Montserrat Fuentes, and Brian J. Reich Abstract Fine particulate matter (PM2.5 ) is a mixture of pollutants that has been

Source URL: www4.stat.ncsu.edu

• Actuarial science
• Estimation theory
• Linear regression
• Nonparametric regression
• Air pollution
• Poisson regression
• Generalized linear model
• Spatial epidemiology
• Statistics
• Regression analysis
• Econometrics

MASTER THESIS IN MICRODATA ANALYSIS Computation and application of the spatial zero inflated count response Author: Shengqiang Guo

Source URL: www.statistics.du.se

• Econometrics
• Actuarial science
• Statistical methods
• Generalized linear model
• Linear regression
• Mixed model
• Least squares
• Random effects model
• Poisson distribution
• Statistics
• Regression analysis
• Statistical models

0.1 probit.mixed: Mixed effects probit Regression Use generalized multi-level linear regression if you have covariates that are grouped according to one or more classification factors. The probit model is appropriate wh

Source URL: perso.md2t.eu

• Statistical models
• Estimation theory
• Analysis of variance
• Linear regression
• Fixed effects model
• Probit model
• Random effects model
• Linear model
• Normal distribution
• Statistics
• Regression analysis
• Econometrics

COURSE FLYER Introduction to Linear Mixed Effects Models and GLMM with R Bayesian and frequentist approaches Provided by: Highland Statistics Ltd

Source URL: www.highstat.com

• Actuarial science
• Statistical methods
• Analysis of variance
• Generalized linear model
• Count data
• Mixed model
• Linear regression
• Generalized linear mixed model
• ADMB
• Statistics
• Regression analysis
• Econometrics

Linear panel data models: lecture 2 Last lecture 1. Prerequisites for the course: OLS and GLS 2. Panels, pseudo panels (time series of cross-sections)

Source URL: members.chello.nl

• Estimation theory
• Ordinary least squares
• Instrumental variable
• Generalized method of moments
• Fixed effects model
• Estimator
• Covariance
• Least squares
• Variance
• Statistics
• Regression analysis
• Econometrics

Structured Additive Regression Models: An R Interface to BayesX Nikolaus Umlauf, Thomas Kneib, Stefan Lang, Achim Zeileis http://eeecon.uibk.ac.at/~umlauf/

Source URL: bayesr.r-forge.r-project.org

• Semiparametric regression
• Generalized additive model
• Additive model
• Linear regression
• Statistics
• Regression analysis
• Generalized linear model

PDF Document

Source URL: socserv.mcmaster.ca

• Generalized linear model
• Variance inflation factor
• Logistic regression
• Least squares
• Count data
• R
• Simple linear regression
• Outline of regression analysis
• Comparison of general and generalized linear models
• Statistics
• Regression analysis
• Linear regression

Case study: Modelling the mushroom yields using expert opinions Jari Miina Finnish Forest Research Institute (Metla)

Source URL: www.nwfps.eu

• Econometrics
• Actuarial science
• Statistical models
• Linear regression
• Generalized linear model
• Vaccinium
• Analytic Hierarchy Process
• Flora
• Statistics
• Regression analysis