pra1981,github

401f18

Class materials for STATS 401 (Fall 2018) Applied Statistical Methods II

401w18

Statistics 401: Applied Statistical Methods II

531w16

Course materials for Stats 531 Winter 2016 (Analysis of Time Series)

810f19

Course site for STATS 810 "Literature proseminar"

a-bayesian-variable-selection-method-for-skewed-and-heteroscedastic-response

We propose new Bayesian methods with proper theoretical justification for selecting and estimating a sparse regression coefficient vector for skewed heteroscedastic response. Our novel Bayesian procedures effectively estimate the median and other quantile functions, accommodate non-local prior for regression effects without compromising ease of implementation via sampling based tools. We also extend our method to deal with some observations with very large errors. The link for the paper is https://arxiv.org/abs/1602.09100. This repository contains R code to select important variables using Markov Chain Monte Carlo algorithm. The code is available for public use.

advanced-r-solutions

Set of solutions for the Advanced R programming book

appliedstat.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

bayesian-change-point-detection

bayesian-changepoint

Bayesian modeling of a changepoint problem with R implementation

bayesian_online_changepoint_detection

In statistical analysis, change detection or change point detection tries to identify times when the probability distribution of a stochastic process or time series changes. Using Bayesian Method & Inferences, we can perform change point detection with online procedure. This is the code in R for Bayesian Online Change Point Detection by Adams&Mackay (2007).

bcpa

Behavioral Change Point Analysis

biostan-examples

bssn

It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function, moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter Birnbaum-Saunders model based on Skew-Normal Distribution. Additionally, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution.