Regression Analysis With a Dtochastic Design Variable
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Date
2006
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley
Open Access Color
Green Open Access
Yes
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
BIP! Indicators
Impulse
Top 10%
Influence
Top 10%
Popularity
Average
Abstract
In regression models, the design variable has primarily been treated as a nonstochastic variable. In numerous situations, however, the design variable is stochastic. The estimation and hypothesis testing problems in such situations are considered. Real life examples are given.
Description
Sazak, Hakan Savas/0000-0001-6123-1214
ORCID
Keywords
Stochastic Design, Non-Normality, Modified Likelihood, Least Squares, Correlation Coefficient, Hypothesis Testing, Least squares, Statistics and Probability, Correlation coefficient, Hypothesis testing, Non-normality, Stochastic design, Statistics, Probability and Uncertainty, Modified likelihood, Linear regression; mixed models, non-normality, Point estimation, Computational problems in statistics, Parametric hypothesis testing, modified likelihood, least squares, General nonlinear regression, correlation coefficient
Fields of Science
0502 economics and business, 05 social sciences, 0101 mathematics, 01 natural sciences
Citation
Sazak, H., Tiko, ML., Islam, MQ. (2006). Regression analysis with a dtochastic design variable. International Statistical Review, 74(1), 77-88.
WoS Q
Q1
Scopus Q
Q1
OpenCitations Citation Count
17
Source
International Statistical Review
Volume
74
Issue
1
Start Page
77
End Page
88
PlumX Metrics
Citations
CrossRef : 17
Scopus : 21
Captures
Mendeley Readers : 3
SCOPUS™ Citations
22
checked on Feb 23, 2026
Web of Science™ Citations
20
checked on Feb 23, 2026
Page Views
4
checked on Feb 23, 2026
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