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logistic regression

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Modified Logistic Regression: An Approximation to SVM and Its Applications in Large-Scale Text Categorization: Logistic Regression LR has been widely used in statistics for many years, and has received extensive study in machine learning community recently due to its close relations to Support Vector Machines SVM and Ad-a-Boost. This paper uses a modified version of LR to approximate the optimization of SVM by a...; Tags: Carnegie-Mellon University, Approximation, Logistic Regression, Engineering; White papers 2007-12-01
Improving Credit Scoring by Generalized Additive Model: Logistic Regression has been widely used in the financial service industry for credit scoring models. Despite its advantages in easy interpretation and low computing cost, Logistic Regression is under the criticism of failure to model the nonlinear features of the predictor's effect on the dependent variable and therefore might lead...; Tags: Industry, SAS Institute, Logistic Regression, Research & Development, Business Operations; White papers 2007-05-14

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Variable Screening for Multinomial Logistic Regression on Very Large Data Sets as Applied to Direct Response Modeling: Beginning with version 8.2 SAS supports multinomial logistic regression as part of PROC LOGISTIC. As a direct response modeling provider, the authors have found that multinomial logistic regression models often provides with the best solutions. However, using multinomial logistic regression presents some challenges. Programmers are often faced with very large...; Tags: Regression, SAS Institute, Modeling, Research & Development, Business Operations; White papers 2007-05-14
Fixed Effects Regression Methods in SAS: Fixed effects regression methods are used to analyze longitudinal data with repeated measures on both independent and dependent variables. They have the attractive feature of controlling for all stable characteristics of the individuals, whether measured or not. This is accomplished by using only within-individual variation to estimate the regression coefficients....; Tags: SAS Institute; White papers 2006-02-28
Lithium Battery Analysis: Probability of Failure Assessment Using Logistic Regression: Fourteen-hundred rows by 53 columns of vendor cell acceptance data were processed though Logistic Regression using SAS Enterprise Miner EM to find any significant correlation between 52 test output parameters independent variables and the pass/fail outcome for each of the 1,400 battery cells tested. The goal was to find helpful...; Tags: Lithium Battery, SAS Institute, Cell; White papers 2008-03-13

Biosurveillance and Outbreak Detection Using the ARIMA and Logistic Procedures: The main objective of this paper is to show potential usefulness of the combination of AutoregRessive Integrated Moving Average ARIMA models and logistic regression with automatic model selection Time-series analysis with ARIMA provides only one perspective of the information in the surveillance data (i.e. the number of patients as a...; Tags: Patient, SAS Institute, Perspective; White papers 2006-02-28
Marginal Interpretation of Subject-Specific Curves: Logistic-Normal Regression: This paper proposes the percentile curves concept as conditional probabilities curves across representative percentiles of the distribution of curves induced by random effects in a logistic model with random intercepts. The authors extend this concept to a logistic model with random intercepts and slopes and propose a methodology to approximate...; Tags: Concept, SAS Institute; White papers 2007-05-14
A Purposeful Selection of Variables Macro for Logistic Regression: The main problem in any model-building situation is to choose from a large set of covariates those that should be included in the "Best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms embedded...; Tags: Algorithm, SAS Institute, Engineering; White papers 2007-05-14
OLS Regression?, Auto-Regression?, Dynamic Regression?: A Practical Modeling Example in Financial Industry: Regression models are commonly applied when it comes to planning or forecasting. Financial data - due to their historical features of the trend component, cyclical component, and irregular component - might not fit well with traditional regression models. In such cases, an autoregression model or a dynamic regression model will...; Tags: Financial, SAS Institute, Model, Modeling, Regression Model, Financial Accounting, Finance; White papers 2007-05-14
Regression Testing: Testers are under an obligation to carry out regression testing. This is not an obligation to be taken lightly! Failure to address this obligation has lead to many products being released with defects that should have been caught. Defects that would have been caught had the right regression testing strategy...; Tags: Regression, Obligation, Strategy, Management; White papers 2006-08-07
DRS (rar): Least Cubic Method is a new method for data regression analysis. Its expanded Least Square Method, according to the principle, can also apply single factor linear data regression, multi factors linear data regression, single factor non-linear data regression, and multi factors non-linear data regression. This version is the first release...; Tags: Regression, Least Cubic Method, Data Regression; Software downloads 2008-01-28
SAS Macros for Automated Model Selection When the Response Variable Is Nominal: Needs and Solutions Involving PROC CATMOD: SAS does not provide the capability to fit logistic regression models using automated model selection methods, such as forward selection and backwards elimination, when the response variable is nominal. PROC LOGISTIC can provide automated model selection, but is only capable of fitting models with binary or ordinal response variables. This...; Tags: SAS Institute; White papers 2007-05-14
Analysis of Large Hierarchical Data With Multilevel Logistic Modeling Using PROC GLIMMIX: The advantage of using multilevel modeling is that it takes the hierarchical structure of the data into account by specifying random effects at each level of analysis, and thus results in a more conservative inference for the aggregate effect. The study discussed in this paper combines data from two databases...; Tags: Job, SAS Institute, Analysis, Recruitment & Selection, Human Resources, Workforce Management; White papers 2006-02-28
Imputation of Categorical Variables With PROC MI: The most generally applicable imputation method available in PROC MI is the MCMC algorithm which is based on the multivariate normal model. While this method is widely used to impute binary and polytomous data, there is a natural concern about the consequences of violating the normality assumption. This paper uses...; Tags: Method, Variable, SAS Institute; White papers 2005-03-09
Updates to SAS Power and Sample Size Software in SAS/STAT 9.2: Procedures for power analysis and sample size determination were introduced in SAS/STAT 9.1 along with a Web application for performing these analyses. SAS/STAT 9.2 adds analyses for several new designs, enhances some existing analyses, and replaces the Web application with a desktop application for your PC. The new analyses include...; Tags: Software, Regression, SAS Institute, SAS/STAT 9.2; White papers 2008-03-13
An Introduction to Quantile Regression and the QUANTREG Procedure: Ordinary least-squares regression models the relationship between one or more covariates X and the conditional mean of a response variable Y given X = x. In contrast, quantile regression models the relationship between X and the conditional quantiles of Y given X = x, so it is especially useful in...; Tags: Regression, SAS Institute, Healthcare; White papers 2005-03-09
A Bayesian Approach to Bandwidth Selection for Multivariate Kernel Regression With an Application to State-Price Density Estimation: Multivariate kernel regression is an important tool for investigating the relationship between a response and a set of explanatory variables. It is generally accepted that the performance of a kernel regression estimator largely depends on the choice of bandwidth rather than the kernel function. This nonparametric technique has been employed...; Tags: Bandwidth, Bayesian, Multivariate Kernel Regression, Kernel Regression; White papers 2007-11-01
Assessing Within- and Between-Individual Effects Using Correlated Data From an HIV Clinical Trial: Examining risky sexual behavior and substance use in the EXPLORE data set presented some challenging data analysis issues. Given the complexity of the data structure, a thorough picture of the within- and between-individual effects was best seen by the use of conditional logistic regression and generalized estimating equations. The SAS...; Tags: SAS Institute; White papers 2006-02-28
Using Propensity Scores to Adjust for Treatment Selection Bias: Estimating the effect of drug treatment on outcomes requires adjusting for many observed factors, particularly those influencing drug selection. This paper demonstrates the use of PROC LOGISTIC in creating propensity scores to address such potential treatment selection bias. In this example using a pharmacy claims database, this method evaluates the...; Tags: Propensity, Adherence, Treatment Selection; White papers 2007-05-14
Large Margin Non-Linear Embedding: It is common in classification methods to first place data in a vector space and then learn decision boundaries. This paper proposes reversing that process: for fixed decision boundaries, the authors "Learn" the location of the data. This way they do not need a metric or even stronger structure -...; Tags: Method, Author; White papers

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