Regression Modeling Approaches

Slides:

Advertisements

Similar presentations

Chapter 2 Describing Contingency Tables Reported by Liu Qi.

Advertisements

Workshop in R & GLMs: #3 Diane Srivastava University of British Columbia

Additive Models, Trees, etc. Based in part on Chapter 9 of Hastie, Tibshirani, and Friedman David Madigan.

Copula Regression By Rahul A. Parsa Drake University &

- Word counts - Speech error counts - Metaphor counts - Active construction counts Moving further Categorical count data.

Best Model Dylan Loudon. Linear Regression Results Erin Alvey.

Robert Plant != Richard Plant. Sample Data Response, covariates Predictors Remotely sensed Build Model Uncertainty Maps Covariates Direct or Remotely.

Part V The Generalized Linear Model Chapter 16 Introduction.

Use Macro to Enter TP Logo March 11-12, 1999, Nashville, Tennessee Mark Scully, Tillinghast-Towers Perrin The Use of Multivariate Analysis Techniques to.

Maxent interface.

Log-linear and logistic models

CHAPTER 29 Classification and Regression Trees Dean L. Urban From: McCune, B. & J. B. Grace Analysis of Ecological Communities. MjM Software Design,

Generalized Linear Models

Discrete Probability Distributions

Midterm Review. 1-Intro Data Mining vs. Statistics –Predictive v. experimental; hypotheses vs data-driven Different types of data Data Mining pitfalls.

BIOE 293 Quantitative ecology seminar Marm Kilpatrick Steve Munch Spring Quarter 2015.

Today: Lab 9ab due after lecture: CEQ Monday: Quizz 11: review Wednesday: Guest lecture – Multivariate Analysis Friday: last lecture: review – Bring questions.

NR 422- Habitat Suitability Models Jim Graham Spring 2009.

Generalized Linear Models All the regression models treated so far have common structure. This structure can be split up into two parts: The random part:

Linear Model. Formal Definition General Linear Model.

Forecasting Choices. Types of Variable Variable Quantitative Qualitative Continuous Discrete (counting) Ordinal Nominal.

Business Intelligence and Decision Modeling Week 9 Customer Profiling Decision Trees (Part 2) CHAID CRT.

An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes Members: Hung-Yi Lo, Kai-Wei.

Linear Discriminant Analysis and Its Variations Abu Minhajuddin CSE 8331 Department of Statistical Science Southern Methodist University April 27, 2002.

Linear Methods for Classification Based on Chapter 4 of Hastie, Tibshirani, and Friedman David Madigan.

Dealing with continuous variables and geographical information in non life insurance ratemaking Maxime Clijsters.

1 Introduction to Modeling Beyond the Basics (Chapter 7)

Dependent Variable Discrete  2 values – binomial  3 or more discrete values – multinomial  Skewed – e.g. Poisson Continuous  Non-normal.

1 Fighting for fame, scrambling for fortune, where is the end? Great wealth and glorious honor, no more than a night dream. Lasting pleasure, worry-free.

Week 7: General linear models Overview Questions from last week What are general linear models? Discussion of the 3 articles.

Biostatistics Class 3 Probability Distributions 2/15/2000.

 Naïve Bayes  Data import – Delimited, Fixed, SAS, SPSS, OBDC  Variable creation & transformation  Recode variables  Factor variables  Missing.

Introduction We consider the data of ~1800 phenotype measurements Each mouse has a given probability distribution of descending from one of 8 possible.

Introduction to Machine Learning Nir Ailon Lecture 11: Probabilistic Models.

Multivariate analysis (Machine learning) Supervised True answer is known Classification Answer is categorical Regression Answer is continuous (ordered.

Crash course in probability theory and statistics – part 2 Machine Learning, Wed Apr 16, 2008.

Kelci J. Miclaus, PhD Advanced Analytics R&D Manager JMP Life Sciences

Habitat Suitability Models

Who will you trust? Field technicians? Software programmers?

Clustering (1) Clustering Similarity measure Hierarchical clustering

The Use of Multivariate Analysis Techniques to Design a Class Plan

Machine Learning Models

CHAPTER 12 MODELING COUNT DATA: THE POISSON AND NEGATIVE BINOMIAL REGRESSION MODELS Damodar Gujarati Econometrics by Example, second edition.

A priori violations In the following cases, your data violates the normality and homoskedasticity assumption on a priori grounds: (1) count data  Poisson.

Microeconometric Modeling

Generalized Linear Models

STA 216 Generalized Linear Models

Generalized Linear Models

Overview of Supervised Learning

Generalized Linear Models (GLM) in R

Introduction to logistic regression a.k.a. Varbrul

with observed random variables

Log Linear Modeling of Independence

Quantitative Methods What lies beyond?.

Direct or Remotely sensed

Soc 3306a: ANOVA and Regression Models

OVERVIEW OF BAYESIAN INFERENCE: PART 1

Microeconometric Modeling

What is Regression Analysis?

Quantitative Methods What lies beyond?.

Optimal scaling for a logistic regression model with ordinal covariates Sanne JW Willems, Marta Fiocco, and Jacqueline J Meulman Leiden University & Stanford.

Soc 3306a Lecture 11: Multivariate 4

Microeconometric Modeling

Models, parameters and GLMs

Pattern Recognition and Machine Learning Chapter 2: Probability Distributions July chonbuk national university.

Machine Learning – a Probabilistic Perspective

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.

Models, parameters and GLMs

Linear regression model of skill degradation over time.

Generalized Additive Model

Presentation transcript:

Regression Modeling Approaches We’re about to explore approaches to regression/covariate modeling: CART: Classification and Regression Trees GLM: Generalized Linear Models GAM: Generalized Additive Models HEMI 2: Hyper-envelope Modeling Interface MaxEnt: Maximum Entropy

CART: GLM: GAM: HEMI 2 & MaxEnt: Response: Categorical Covariates: Categorical or Continuous GLM: Response: Binary or Continuous (known function: linear, gamma, binomial…) Covariates: Continuous GAM: Response: Virtually any continuous HEMI 2 & MaxEnt: Response: Occurrences (points) Covariates: Continuous (typical) or Categorical

Response Drives the Method Occurrences only (point density): Habitat: MaxEnt, HEMI 2 Density estimators, clustering Binary (presence/absence): Binomial, CART Categorical: CART Continuous: Linear Regression: Linear GLM: Linear, Poisson, Gamma GAM: Virtually any continuous

Building Models Selecting the method Selecting the covariates/predictors (“Model Selection”) Optimizing the coefficients/parameters of the model 9bytez.com:Old School Hobbies: Building Models by Hand