On the convergence of least squares estimates in white noise

1994 ◽  
Vol 39 (2) ◽  
pp. 364-368 ◽  
Author(s):  
K. Nassiri-Toussi ◽  
W. Ren
Keyword(s):  
White Noise ◽  
Least Squares ◽  
Least Squares Estimates

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.

scholarly journals A Robust Regression by Using Huber Estimator and Tukey Bisquare Estimator for Predicting Availability of Corn in Karanganyar Regency, Indonesia

2018 ◽  
Vol 1 (1) ◽  
pp. 37
Author(s):  
Hasih Pratiwi ◽  
Yuliana Susanti ◽  
Sri Sulistijowati Handajani
Keyword(s):  
Least Squares ◽  
Robust Regression ◽  
Likelihood Estimation ◽  
Corn Production ◽  
Least Squares Fit ◽  
Class Of Estimators ◽  
M Estimation ◽  
Heavy Tailed ◽  
Least Squares Estimates ◽  
Huber Estimator

Linear least-squares estimates can behave badly when the error distribution is not normal, particularly when the errors are heavy-tailed. One remedy is to remove influential observations from the least-squares fit. Another approach, robust regression, is to use a fitting criterion that is not as vulnerable as least squares to unusual data. The most common general method of robust regression is M-estimation. This class of estimators can be regarded as a generalization of maximum-likelihood estimation. In this paper we discuss robust regression model for corn production by using two popular estimators; i.e. Huber estimator and Tukey bisquare estimator.<br />Keywords : robust regression, M-estimation, Huber estimator, Tukey bisquare estimator

Discussion of “Least Squares Estimates of BOD Parameters“

1982 ◽  
Vol 108 (1) ◽  
pp. 215-217
Author(s):  
Marshall E. Jennings ◽  
Steven C. McCutcheon ◽  
Kathleen M. Flynn
Keyword(s):  
Least Squares ◽  
Least Squares Estimates

INFLUENCE OF MATRIX ERRORS ON PARAMETER ESTIMATES BY THE LEAST SQUARES METHOD

2021 ◽  
Author(s):  
V. A. Galanina ◽  
◽  
L. A. Reshetov ◽  
M. V. Sokolovskay ◽  
A. E. Farafonova ◽  
...  
Keyword(s):  
Least Squares ◽  
Linear Model ◽  
Least Squares Method ◽  
Statistical Characteristics ◽  
Parameter Estimates ◽  
Model Matrix ◽  
Least Squares Estimates

The paper investigates the effect of distorsions of the linear model matrix on the statistical characteristics of the least squares estimates.

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