Results 51 to 60 of about 75,182 (196)

a simulation comparison of Ridge regression estimators with Lars

پژوهش‌های ریاضی, 2022
Introduction Regression analysis is a common method for modeling relationships between variables. Usually Ordinary Least Squares method is applied to estimate regression model parameters.
Roshanak Alimohammadi, Jaleh Bahari
doaj  

Asymptotic Bias of Ordinary Least Squares Estimator for Multivariate Autoregressive Models [PDF]

, 1981
Naoto Kunitomo, Taku Yamamoto
core   +2 more sources

Instrumental Variable Interpretation of Cointegration with Inference Results for Fractional Cointegration [PDF]

, 2002
In this paper we propose an alternative characterization of the central notion of cointegration, exploiting the relationship between the autocovariance and the cross-covariance functions of the series.
Aparicio, Felipe M., Escribano, Álvaro, Mármol, Francesc   +2 more
core   +3 more sources

Comparing ols and rank-based estimation techniques for production analysis: An application to Ghanaian maize farms.

Apstract: Applied Studies in Agribusiness and Commerce, 2016
This paper introduces the rank-based estimation method to modelling the Cobb-Douglas production function as an alternative to the least squares approach.
Henry De-Graft Acquah
doaj   +1 more source

Estimation parameters using bisquare weighted robust ridge regression BRLTS estimator in the presence of multicollinearity and outliers [PDF]

, 2015
This study presents an improvement to robust ridge regression estimator. We proposed two methods Bisquare ridge least trimmed squares (BRLTS) and Bisquare ridge least absolute value (BRLAV) based on ridge least trimmed squares RLTS and ridge least ...
Adnan, Robiah, Kafi, Dano Pati, Md. Jedi, Muhamad Alias, Rasheed, Abdulkadir Bello   +3 more
core   +1 more source

Two Kantorovich-type inequalities and efficiency comparisons between the OLSE and BLUE

Journal of Inequalities and Applications, 2002
We first establish two matrix-determinant Kantorovich-type inequalities. Then we introduce two efficiency criteria and make efficiency comparisons between the ordinary least squares estimator and best linear unbiased estimator in linear models.
King Maxwell L, Liu Shuangzhe
doaj  

The Comparison Between Different Approaches to Overcome the Multicollinearity Problem in Linear Regression Models

Ibn Al-Haitham Journal for Pure and Applied Sciences, 2018
    In the presence of multi-collinearity problem, the parameter estimation method based on the ordinary least squares procedure is unsatisfactory. In 1970, Hoerl and Kennard insert analternative method labeled as estimator of ridge regression.
Hazim Mansoor Gorgees, Fatimah Assim Mahdi   +1 more
doaj   +1 more source

Kernel-Based Regularized Least Squares in R (KRLS) and Stata (krls)

Journal of Statistical Software, 2017
The Stata package krls as well as the R package KRLS implement kernel-based regularized least squares (KRLS), a machine learning method described in Hainmueller and Hazlett (2014) that allows users to tackle regression and classification problems without
Jeremy Ferwerda, Jens Hainmueller, Chad J. Hazlett   +2 more
doaj   +1 more source

Estimating the intercept in an orthogonally blocked experiment when the block effects are random. [PDF]

: For an orthogonally blocked experiment, Khuri (1992) has shown that the ordinary least squares estimator and the generalized least squares estimator of the factor effects in a response surface model with random block effects coincide.
Goos, Peter, Vandebroek, Martina
core  

Effects of a single outlier on the coefficient of determination: an empirical study [PDF]

, 2014
This article investigates the effects of outliers on the coefficient of determination, R2 which is computed by Ordinary Least Squares (OLS) estimator. It is now evident that the OLS is greatly affected by outliers and hence the R2 is also affected.
Fitrianto, Anwar, Hydara, Kutub, Midi, Habshah, Rana, Md. Sohel   +3 more
core   +1 more source