Multiple regression for continuous response variables

2008 ◽  
pp. 53-74
Keyword(s):  
Multiple Regression ◽  
Continuous Response ◽  
Response Variables

scholarly journals Support Vector Machines and Support Vector Regression

2022 ◽  
pp. 337-378
Author(s):  
Osval Antonio Montesinos López ◽  
Abelardo Montesinos López ◽  
Jose Crossa
Keyword(s):  
Support Vector Machines ◽  
Plant Breeding ◽  
Genomic Prediction ◽  
Kernel Functions ◽  
Support Vector ◽  
Continuous Response ◽  
Support Vector Classifier ◽  
Vector Machines ◽  
Categorical Response ◽  
Response Variables

AbstractIn this chapter, the support vector machines (svm) methods are studied. We first point out the origin and popularity of these methods and then we define the hyperplane concept which is the key for building these methods. We derive methods related to svm: the maximum margin classifier and the support vector classifier. We describe the derivation of the svm along with some kernel functions that are fundamental for building the different kernels methods that are allowed in svm. We explain how the svm for binary response variables can be expanded for categorical response variables and give examples of svm for binary and categorical response variables with plant breeding data for genomic selection. Finally, general issues for adopting the svm methodology for continuous response variables are provided, and some examples of svm for continuous response variables for genomic prediction are described.

scholarly journals PEMODELAN REGRESI SPLINE (Studi Kasus: Herpindo Jaya Cabang Ngaliyan)

2015 ◽  
Vol 4 (3) ◽  
pp. 110
Author(s):  
I MADE BUDIANTARA PUTRA ◽  
I GUSTI AYU MADE SRINADI ◽  
I WAYAN SUMARJAYA
Keyword(s):  
Multiple Regression ◽  
Regression Function ◽  
Predictor Variables ◽  
Coefficient Of Determination ◽  
Purchasing Decisions ◽  
Product Price ◽  
Nonparametric Approach ◽  
The Relationship ◽  
Response Variables

Regression analysis is a method of data analysis to describe the relationship between response variables and predictor variables. There are two approaches to estimating the regression function. They are parametric and nonparametric approaches. The parametric approach is used when the relationship between the predictor variables and the response variables are known or the shape of the regression curve is known. Meanwhile, the nonparametric approach is used when the form of the relationship between the response and predictor variables is unknown or no information about the form of the regression function. The aim of this study are to determine the best spline nonparametric regression model on data of quality of the product, price, and advertising on purchasing decisions of Yamaha motorcycle with optimal knots point and to compare it with the multiple regression linear based on the coefficient of determination (R2) and mean square error (MSE). Optimal knot points are defined by two point knots. The result of this analysis is that for this data multiple regression linear is better than the spline regression one.

scholarly journals Modeling continuous response variables using ordinal regression

2017 ◽  
Vol 36 (27) ◽  
pp. 4316-4335 ◽  
Author(s):  
Qi Liu ◽  
Bryan E. Shepherd ◽  
Chun Li ◽  
Frank E. Harrell
Keyword(s):  
Ordinal Regression ◽  
Continuous Response ◽  
Response Variables

Comparing Predictors in Multivariate Regression Models: An Extension of Dominance Analysis

2006 ◽  
Vol 31 (2) ◽  
pp. 157-180 ◽  
Author(s):  
Razia Azen ◽  
David V. Budescu
Keyword(s):  
Multiple Regression ◽  
Multivariate Regression ◽  
Regression Models ◽  
Multiple Response ◽  
Relative Importance ◽  
Multivariate Models ◽  
Minimal Set ◽  
Dominance Analysis ◽  
Response Variables

Dominance analysis (DA) is a method used to compare the relative importance of predictors in multiple regression. DA determines the dominance of one predictor over another by comparing their additional R2 contributions across all subset models. In this article DA is extended to multivariate models by identifying a minimal set of criteria for an appropriate generalization of R2 to the case of multiple response variables. The DA results obtained by univariate regression (with each criterion separately) are analytically compared with results obtained by multivariate DA and illustrated with an example. It is shown that univariate dominance does not necessarily imply multivariate dominance (and vice versa), and it is recommended that researchers who wish to account for the correlation among the response variables use multivariate DA to determine the relative importance of predictors.

scholarly journals Reproducing Kernel Hilbert Spaces Regression and Classification Methods

2022 ◽  
pp. 251-336
Author(s):  
Osval Antonio Montesinos López ◽  
Abelardo Montesinos López ◽  
Jose Crossa
Keyword(s):  
Hilbert Spaces ◽  
Reproducing Kernel ◽  
Reproducing Kernel Hilbert Spaces ◽  
Statistical Machine Learning ◽  
Regression Methods ◽  
Continuous Response ◽  
Fixed Model ◽  
Kernel Hilbert Spaces ◽  
Categorical Response ◽  
Response Variables

AbstractThe fundamentals for Reproducing Kernel Hilbert Spaces (RKHS) regression methods are described in this chapter. We first point out the virtues of RKHS regression methods and why these methods are gaining a lot of acceptance in statistical machine learning. Key elements for the construction of RKHS regression methods are provided, the kernel trick is explained in some detail, and the main kernel functions for building kernels are provided. This chapter explains some loss functions under a fixed model framework with examples of Gaussian, binary, and categorical response variables. We illustrate the use of mixed models with kernels by providing examples for continuous response variables. Practical issues for tuning the kernels are illustrated. We expand the RKHS regression methods under a Bayesian framework with practical examples applied to continuous and categorical response variables and by including in the predictor the main effects of environments, genotypes, and the genotype ×environment interaction. We show examples of multi-trait RKHS regression methods for continuous response variables. Finally, some practical issues of kernel compression methods are provided which are important for reducing the computation cost of implementing conventional RKHS methods.

scholarly journals An evaluation of differential susceptibility of barley varieties to lodging under varying environmental conditions

2006 ◽  
Vol 144 (6) ◽  
pp. 525-531
Author(s):  
T. HURME ◽  
J. ÖFVERSTEN ◽  
L. JAUHIAINEN
Keyword(s):  
Linear Models ◽  
Differential Susceptibility ◽  
Environmental Variation ◽  
Performance Characteristics ◽  
Binary Response ◽  
Conditional Mean ◽  
Continuous Response ◽  
Crop Varieties ◽  
Local Yielding ◽  
Response Variables

Information on the variation in susceptibility to lodging between barley varieties under different environments can be used for local prediction. It can also be used to identify varieties that are robust to environmental variation. Efficient methods to obtain analogous information on the local yielding capacity of crop varieties have recently been established. The present paper extends the use of these methods for the analysis of dichotomously measured performance characteristics, the susceptibility to lodging, in particular. The procedures examined were based on generalized linear models in which the expected conditional mean of the susceptibility to lodging was used as an explanatory variable to express the environmental effects of a given environment. Through the use of logistic transformation the approach extends previous methods developed for continuous response variables to binary response variables. Models were subsequently used to obtain measures of susceptibility to lodging of each barley variety in terms of environmental variation. While the emphasis is restricted to lodging, similar methods can also be applied to other performance characteristics.

scholarly journals APLIKASI MULTIVARIATE MULTIPLE REGRESSION UNTUK MENDUGA FAKTOR-FAKTOR YANG MEMENGARUHI KESEJAHTERAAN MASYARAKAT

2014 ◽  
Vol 3 (3) ◽  
pp. 100
Author(s):  
PUTU EKA SWASTINI ◽  
I KOMANG GDE SUKARSA ◽  
I PUTU EKA N. KENCANA
Keyword(s):  
Human Development ◽  
Multiple Regression ◽  
Human Development Index ◽  
Predictor Variables ◽  
Final Model ◽  
Welfare Issue ◽  
Multivariate Multiple Regression ◽  
The Relationship ◽  
Single Set ◽  
Response Variables

This essay aimed to apply the Multivariate Multiple Regression (MMR) methodfor the welfare issue. The predictor variables in the model are 18 indicators of welfare according to Indonesian Central Bureau of Statistic (BPS) and  the response variables are Human Development Index (IPM), Gross Regional Domestic Product (PDRB), and Regional Crime Index (IKD). In modeling the relationship between q responses and a single set of predictor variables , MMR assumed each pairs of two response variables were correlated and its distribution follows normal multivariate. Based on the result of MMR, we obtained six out of 18 predictor variables simultaneously affect IPM and  PDRB. The final model showed the association between those variables very closed to 100 percent.

Language and Literacy Predictors of Dialect Density Among School-Age African American Children From Two Geographic Regions

2020 ◽  
Vol 51 (3) ◽  
pp. 807-820
Author(s):  
Lena G. Caesar ◽  
Marie Kerins
Keyword(s):  
African American ◽  
Multiple Regression ◽  
Oral Language ◽  
East Coast ◽  
Geographic Region ◽  
Literacy Skills ◽  
School Age ◽  
Regression Analyses ◽  
Geographic Regions ◽  
American Children

Purpose The purpose of this study was to investigate the relationship between oral language, literacy skills, age, and dialect density (DD) of African American children residing in two different geographical regions of the United States (East Coast and Midwest). Method Data were obtained from 64 African American school-age children between the ages of 7 and 12 years from two geographic regions. Children were assessed using a combination of standardized tests and narrative samples elicited from wordless picture books. Bivariate correlation and multiple regression analyses were used to determine relationships to and relative contributions of oral language, literacy, age, and geographic region to DD. Results Results of correlation analyses demonstrated a negative relationship between DD measures and children's literacy skills. Age-related findings between geographic regions indicated that the younger sample from the Midwest outscored the East Coast sample in reading comprehension and sentence complexity. Multiple regression analyses identified five variables (i.e., geographic region, age, mean length of utterance in morphemes, reading fluency, and phonological awareness) that accounted for 31% of the variance of children's DD—with geographic region emerging as the strongest predictor. Conclusions As in previous studies, the current study found an inverse relationship between DD and several literacy measures. Importantly, geographic region emerged as a strong predictor of DD. This finding highlights the need for a further study that goes beyond the mere description of relationships to comparing geographic regions and specifically focusing on racial composition, poverty, and school success measures through direct data collection.

The Relationship of Compliance with Coping Strategies and Self-Esteem

2003 ◽  
Vol 19 (2) ◽  
pp. 117-123 ◽  
Author(s):  
Gisli H. Gudjonsson ◽  
Jon Fridrik Sigurdsson
Keyword(s):  
Sex Differences ◽  
Coping Strategies ◽  
Multiple Regression ◽  
Sex Difference ◽  
Test Scores ◽  
Self Esteem ◽  
Stressful Situation ◽  
Men And Women ◽  
Relationship Of ◽  
The Relationship

Summary: The Gudjonsson Compliance Scale (GCS), the COPE Scale, and the Rosenberg Self-Esteem Scale were administered to 212 men and 212 women. Multiple regression of the test scores showed that low self-esteem and denial coping were the best predictors of compliance in both men and women. Significant sex differences emerged on all three scales, with women having lower self-esteem than men, being more compliant, and using different coping strategies when confronted with a stressful situation. The sex difference in compliance was mediated by differences in self-esteem between men and women.

Sign in / Sign up

Export Citation Format

Share Document