Computing Iterative Weighted Least Squares Estimates of Genetic Variance Components

Biometrics ◽  
1977 ◽  
Vol 33 (2) ◽  
pp. 375 ◽  
Author(s):  
P. L. Cornelius ◽  
J. Byars
Keyword(s):  
Least Squares ◽  
Variance Components ◽  
Genetic Variance ◽  
Weighted Least Squares ◽  
Least Squares Estimates

scholarly journals GENETIC MODELS FOR THE ANALYSIS OF DATA FROM THE FAMILIES OF IDENTICAL TWINS

Genetics ◽  
1976 ◽  
Vol 83 (4) ◽  
pp. 811-826
Author(s):  
Walter E Nance ◽  
Linda A Corey
Keyword(s):  
Least Squares ◽  
Maternal Effects ◽  
Variance Components ◽  
Quantitative Traits ◽  
Genetic Variance ◽  
Weighted Least Squares ◽  
Identical Twins ◽  
Genetic Models ◽  
Family Unit ◽  
Least Squares Estimates

ABSTRACT Genetic models are described which exploit the unique relationships that exist within the families of identical twins to obtain weighted least squares estimates of additive, dominance and epistatic components of genetic variance as well as estimates of the contributions of X-linked genes, maternal effects and three sources of environmental variation. Since all of the relationships required to achieve a resolution of these variance components are contained within each family unit, the model would appear to be superior to previous approaches to the analysis of quantitative traits in man.

scholarly journals Improved Dividend Estimation from Intraday Quotes

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 95
Author(s):  
Pontus Söderbäck ◽  
Jörgen Blomvall ◽  
Martin Singull
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Statistical Significance ◽  
Ordinary Least Squares ◽  
Options Market ◽  
Market Data ◽  
Out Of Sample ◽  
Intraday Data ◽  
Least Squares Estimates ◽  
Estimation Quality

Liquid financial markets, such as the options market of the S&P 500 index, create vast amounts of data every day, i.e., so-called intraday data. However, this highly granular data is often reduced to single-time when used to estimate financial quantities. This under-utilization of the data may reduce the quality of the estimates. In this paper, we study the impacts on estimation quality when using intraday data to estimate dividends. The methodology is based on earlier linear regression (ordinary least squares) estimates, which have been adapted to intraday data. Further, the method is also generalized in two aspects. First, the dividends are expressed as present values of future dividends rather than dividend yields. Second, to account for heteroscedasticity, the estimation methodology was formulated as a weighted least squares, where the weights are determined from the market data. This method is compared with a traditional method on out-of-sample S&P 500 European options market data. The results show that estimations based on intraday data have, with statistical significance, a higher quality than the corresponding single-times estimates. Additionally, the two generalizations of the methodology are shown to improve the estimation quality further.

Stein-rule estimation of coefficients for 18 eastern hardwood cubic volume equations

1986 ◽  
Vol 16 (2) ◽  
pp. 249-255 ◽  
Author(s):  
Edwin J. Green ◽  
William E. Strawderman
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Weighted Average ◽  
Total Sample ◽  
Ordinary Least Squares ◽  
Validation Data ◽  
Regression Parameters ◽  
Volume Equation ◽  
Mean Square Errors ◽  
Least Squares Estimates

A Stein-rule estimator, which shrinks least squares estimates of regression parameters toward their weighted average, was employed to estimate the coefficient in the constant form factor volume equation for 18 species simultaneously. The Stein-rule procedure was applied to ordinary least squares estimates and weighted least squares estimates. Simulation tests on independent validation data sets revealed that the Stein-rule estimates were biased, but predicted better than the corresponding least squares estimates. The Stein-rule procedures also yielded lower estimated mean square errors for the volume equation coefficient than the corresponding least squares procedure. Different methods of withdrawing sample data from the total sample available for each species revealed that the superiority of Stein-rule procedures over least squares decreased as the sample size increased and that the Stein-rule procedures were robust to unequal sample sizes, at least on the scale studied here.

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