scholarly journals D-optimal experimental designs for linear multiple regression under heteroscedastic observations

2020 ◽  
pp. 59-67
Author(s):  
Valery P. Kirlitsa
Keyword(s):  
Multiple Regression ◽  
Experimental Designs ◽  
Linear Multiple Regression ◽  
Exact Number ◽  
Different Types

The problem of construction of «continuous» (number of observations is not fixed) and «exact» (number of observations is fixed) D-optimal experimental designs for linear multiple regression in the case when variance of errors of observations depends on regressor value is studied in this paper. Families of functions that determine heteroscedastic observations are found for which it is possible to construct «continuous» and «exact» D-optimal experimental designs. «Continuous» D-optimal experimental designs under four different types of heteroscedasticity are constructed for linear multiple regression with three regressors.

scholarly journals Construction D-optimal designs of experiments for linear multiple regression with heteroscedastic observations

2019 ◽  
pp. 27-33
Author(s):  
Valery P. Kirlitsa
Keyword(s):  
Multiple Regression ◽  
Optimal Designs ◽  
Linear Multiple Regression ◽  
Different Types ◽  
Designs Of Experiments ◽  
Class Of Functions

In article the problem of construction exact D-optimal designs of experiments for linear multiple regression in a case when variance of errors of observations depend on a point in which is made is investigated. Class of functions which describe change variance of heteroscedastic observations is defined for which it is possible construct D-optimal continues designs of experiments. For linear multiple regression with three factors it is constructed five different types of D-optimal continues designs of experiments with heteroscedastic observations. For each of these types the own class of functions describing change variance of observations is defined.

scholarly journals Enterprise Value and Intellectual Capital: Study of BSE 500 Firms

2018 ◽  
Vol 7 (2) ◽  
pp. 123 ◽  
Author(s):  
Priti Sharma
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Intellectual Capital ◽  
Pearson Correlation ◽  
Market Value ◽  
Linear Multiple Regression ◽  
Listed Firms ◽  
Customer Capital ◽  
Almost All

The purpose of this paper is to estimate the intellectual capital coefficient of the firms under study and to study the relationship, if any between intellectual capital and intellectual capital and its constituents. In this empirical paper, analytical research design has been used. Pulic’s VAIC (modified) has been used to estimate the intellectual capital of BSE S&P 500 listed firms from 2007-2016. The data has been collected from CMIE and collected data has been analyzed using Pearson correlation and linear multiple regression analysis using CMIE PROWESS. Findings show that almost all firms under study have a good VAIC score means above 4 and the top VAIC scorer firms were mainly from refinery, metal, cement, steel, tobacco. Correlation analysis and Linear multiple regression analysis show that M/B ratio has a significant relationship with VACA, VAHU, Research and Development (Innovation capital) and Advertisement expenses (customer capital). Year-wise results depicts that value of adjusted R2 is increasing, in 2007 it was just .164 and in the year 2016 it is .607 which infers that VAIC’s role is improving in measuring the market value of firms under study. Year wise analysis shows that adjusted R2 is improving, so findings may serve as significant input for the firms to use intellectual capital as the main factor for improving the market value of firms. This paper will definitely contribute to the existing literature.

Function development for appraising brittleness of intact rocks using genetic programming and non-linear multiple regression models

2016 ◽  
Vol 33 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Manoj Khandelwal ◽  
Roohollah Shirani Faradonbeh ◽  
Masoud Monjezi ◽  
Danial Jahed Armaghani ◽  
Muhd Zaimi Bin Abd Majid ◽  
...  
Keyword(s):  
Genetic Programming ◽  
Multiple Regression ◽  
Regression Models ◽  
Linear Multiple Regression ◽  
Multiple Regression Models ◽  
Non Linear

Palatal Morphology and Type of Clefting

1996 ◽  
Vol 33 (6) ◽  
pp. 477-482 ◽  
Author(s):  
Pauli V.J. Kilpeläinen ◽  
Maija T. Laine-Alava ◽  
Seppo Lammi
Keyword(s):  
Cleft Palate ◽  
Developmental Stage ◽  
Multiple Regression ◽  
Regression Analyses ◽  
Missing Teeth ◽  
Secondary Palate ◽  
Different Types ◽  
Primary Palate

In this study, the variation in palatal dimensions, particularly for height, width, and depth, were evaluated in subjects with different types of cleft palate using the moiré technique. The sample consisted of 95 subjects with cleft palate and 68 noncleft individuals. Effects of cleft type, gender, developmental stage of the dentition, missing teeth, and 11 skeletal and pharyngeal cephalometric variables on palatal dimensions were assessed using multiple-regression analyses. The typing of clefts had an effect on the all palatal dimensions. The palate was shallower in subjects with clefts involving the secondary palate than in other types of clefts or in none left individuals. The palatal width was decreased in subjects with cleft in the primary palate, and the palatal depth was affected by all cleft types except submucous cleft. Gender, developmental stage of the dentition, missing teeth, and some of the cephalometric variables also had a certain effect on the palatal dimensions.

scholarly journals Hierarchical Organization of Reading Subskills: Statistical Approaches

1975 ◽  
Vol 7 (3) ◽  
pp. 223-228 ◽  
Author(s):  
R. G. Stennett ◽  
P. C. Smythe ◽  
Madeline Hardy
Keyword(s):  
Cluster Analysis ◽  
Factor Analysis ◽  
Multiple Regression ◽  
Analysis Of Variance ◽  
Hierarchical Organization ◽  
Experimental Designs ◽  
Stepwise Multiple Regression ◽  
Scaling Methods ◽  
Statistical Approaches ◽  
Reading Subskills

This article describes a number of alternate methodological solutions to the problem of isolating reading subskills and establishing the nature of their hierarchical organization. The authors briefly review and evaluate Stepwise Multiple Regression, Factor Analysis, Cluster Analysis, Scaling Methods, Analysis of Variance and Transfer Designs, as potential tools in research on the hierarchical organization of reading subskills. With the exception of transfer-type experimental designs, none of the approaches seems satisfactory, the authors conclude.

Multivariate Multiple Regression Analyses: A Permutation Method for Linear Models

2002 ◽  
Vol 91 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Paul W. Mielke ◽  
Kenneth J. Berry
Keyword(s):  
Multiple Regression ◽  
Euclidean Distance ◽  
Linear Models ◽  
Latin Square ◽  
Experimental Designs ◽  
Regression Analyses ◽  
Permutation Procedure ◽  
Multivariate Extension ◽  
Multivariate Multiple Regression ◽  
Euclidean Distances

A multivariate extension of a univariate procedure for the analysis of experimental designs is presented. A Euclidean-distance permutation procedure is used to evaluate multivariate residuals obtained from a regression algorithm, also based on Euclidean distances. Applications include various completely randomized and randomized block experimental designs such as one-way, Latin square, factorial, nested, and split-plot designs, with and without covariates. Unlike parametric procedures, the only required assumption is the randomization of subjects to treatments.

Sign in / Sign up

Export Citation Format

Share Document