We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byOsborn Burns
Modified about 1 year ago
Penalized Maximum Likelihood Logistic Regression Joseph Coveney Cobridge Co., Ltd.
Topics Separation in Logistic Regression Approaches to Separation Firth’s Bias-reduced GLMs firthlogit: syntax and examples Caveats and to-do’s
Separation in Logistic Regression
Dataset adapted from D. W. Hosmer and S. Lemeshow, Applied Logistic Regression Second Edition. (New York: John Wiley & Sons, 2000), pp. 138–39. Complete Separation
Quasi-complete Separation Dataset adapted from D. W. Hosmer and S. Lemeshow, Applied Logistic Regression Second Edition. (New York: John Wiley & Sons, 2000), pp. 138–39.
Approaches to Separation Remove predictors –Pool groups –Remove interaction terms Gather more data Use alternatives
Exact Logistic Regression
But... Dataset from D. M. Potter A permutation test for inference in logistic regression with small- and moderate-sized data sets. Statistics in Medicine 24:693–708.
 D. Firth Bias reduction in maximum likelihood estimates. Biometrika 80:27–38.
But... redux, continued
Profile Likelihood Ratio CIs
Caveats Profile Penalized Likelihood CIs Small-sample Behavior
G. Heinze and M. Ploner, A SAS macro, S-PLUS library and R package to perform logistic regression without convergence problems. Technical Report 2/2004. Medical University of Vienna. p. 36.
To-do’s Profile Penalized Likelihood CIs Modify ml d0
Oyindamola B. Yusuf (Ph.D) Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria 1.
Does your logistic regression model suck?. PERFECTION!
1 Overview of Logistics Regression and its SAS implementation Logistics regression is widely used nowadays in finance, marketing research and clinical.
Colloids and Interfaces with Surfactants and Polymers, Second Edition James Goodwin © 2009 John Wiley & Sons, Ltd 1.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Statistical inference.
HSRP 734: Advanced Statistical Methods July 17, 2008.
1 GCRC Data Analysis with SPSS Workshop Session 5 Follow Up on FEV data Binary and Categorical Outcomes 2x2 tables 2xK tables JxK tables Logistic Regression.
The Cox proportional hazards model (Cox Regression Model)
Applied Epidemiologic Analysis - P8400 Fall 2002 Labs 6 & 7 Case-Control Analysis ----Logistic Regression Henian Chen, M.D., Ph.D.
Logistic Regression I Outline Introduction to maximum likelihood estimation (MLE) Introduction to Generalized Linear Models The simplest logistic regression.
AMMBR II Gerrit Rooks. Checking assumptions in logistic regression Hosmer & Lemeshow Residuals Multi-collinearity Cooks distance.
1 G Lect 11W Logistic Regression Review Maximum Likelihood Estimates Probit Regression and Example Model Fit G Multiple Regression Week 11.
Calculus, 8/E by Howard Anton, Irl Bivens, and Stephen Davis Copyright © 2005 by John Wiley & Sons, Inc. All rights reserved. Definition (p. 626)
How to Handle Missing Values in Multivariate Data By Jeff McNeal & Marlen Roberts 1.
Logistic Regression. Aims When and Why do we Use Logistic Regression? – Binary – Multinomial Theory Behind Logistic Regression – Assessing the Model –
Psychology 202a Advanced Psychological Statistics November 12, 2015.
Logistic Regression Biostatistics 510 March 15, 2007 Vanessa Perez.
Maximum Likelihood We have studied the OLS estimator. It only applies under certain assumptions In particular, ~ N(0, 2 ) But what if the sampling distribution.
Examples of Designed Experiments With Nonnormal Responses SHARON L. LEWIS, DOUGLAS C. MONTGOMERY and RAYMOND H. MYERS Journal of Quality Technology, 33,
Copyright © Leland Stanford Junior University. All rights reserved. Warning: This presentation is protected by copyright law and international.
1 Expectation Maximization Algorithm José M. Bioucas-Dias Instituto Superior Técnico 2005.
April 4 Logistic Regression –Lee Chapter 9 –Cody and Smith 9:F.
1 Introduction to Modeling Beyond the Basics (Chapter 7)
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Statistical inference (Sec. )
Mathematical Model for the Law of Comparative Judgment in Print Sample Evaluation Mai Zhou Dept. of Statistics, University of Kentucky Luke C.Cui Lexmark.
LOGISTIC REGRESSION Binary dependent variable (pass-fail) Odds ratio: p/(1-p) eg. 1/9 means 1 time in 10 pass, 9 times fail Log-odds ratio: y = ln[p/(1-p)]
Ordinal Regression Analysis: Fitting the Proportional Odds Model Using Stata and SAS Xing Liu Neag School of Education University of Connecticut.
Introduction to Logistic Regression Rachid Salmi, Jean-Claude Desenclos, Alain Moren, Thomas Grein.
Logistic regression analysis predicting feeding behavior at first follow- up using the total BAPT score (Trimmed model) Hosmer and Lemeshow Goodness of.
Logistic Regression II Simple 2x2 Table (courtesy Hosmer and Lemeshow) Exposure=1Exposure=0 Disease = 1 Disease = 0.
1 ASSESSING THE PERFORMANCE OF MEDICAL DIAGNOSTIC SYSTEMS: THE RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE JOSEPH GEORGE CALDWELL, PH.D. 27 FEBRUARY.
Chapter 4. The Normality Assumption: CLassical Normal Linear Regression Model (CNLRM)
Topics Introduction to Stata – Files / directories – Stata syntax – Useful commands / functions Logistic regression analysis with Stata – Estimation –
Sample size and power estimation when covariates are measured with error Michael Wallace London School of Hygiene and Tropical Medicine.
1 Introduction to Survey Data Analysis Linda K. Owens, PhD Assistant Director for Sampling & Analysis Survey Research Laboratory University of Illinois.
An Introduction to Genetic Algorithm (GA) By: Dola Pathak For: STAT 992:Computational Statistics SPRING
Birthweight (gms) BPDNProp Total BPD (Bronchopulmonary Dysplasia) by birth weight Proportion.
Kernel Methods Jong Cheol Jeong. Out line 6.1 One-Dimensional Kernel Smoothers Local Linear Regression Local Polynomial Regression 6.2 Selecting.
APPLICATION OF MULTIVARIATE ANALYSES TO FIND PREDICTORS OF MULTIPLE GESTATIONS FOLLOWING IN VITRO FERTILIZATION Krisztina Boda and Péter Kovács Department.
Qualitative and Limited Dependent Variable Models ECON 6002 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s notes.
A shared random effects transition model for longitudinal count data with informative missingness Jinhui Li Joint work with Yingnian Wu, Xiaowei Yang.
Linear Regression Analysis 5E Montgomery, Peck & Vining 1 Chapter 13 Generalized Linear Models.
Personal Lines Actuarial Research Department Generalized Linear Models CAGNY Wednesday, November 28, 2001 Keith D. Holler Ph.D., FCAS, ASA, ARM, MAAA.
Chapter 61Introduction to Statistical Quality Control, 5th Edition by Douglas C. Montgomery. Copyright (c) 2005 John Wiley & Sons, Inc.
24-04 Excerpted from Meggs’ History of Graphic Design, Fourth Edition. Copyright 2005, All rights reserved. Published by John Wiley & Sons, Inc.
Information geometry of Statistical inference with selective sample S. Eguchi, ISM & GUAS This talk is a part of co-work with J. Copas, University of Warwick.
Logistic Regression. Conceptual Framework - LR Dependent variable: two categories with underlying propensity (yes/no) (absent/present) Independent variables:
Likelihood Methods in Ecology November 16 th – 20 th, 2009 Millbrook, NY Instructors: Charles Canham and María Uriarte Teaching Assistant Liza Comita.
© 2017 SlidePlayer.com Inc. All rights reserved.