Accounting for Skewed or One-Sided Measurement Error in the Dependent Variable

Abstract While classical measurement error in the dependent variable in a linear regression framework results only in a loss of precision, nonclassical measurement error can lead to estimates, which are biased and inference which lacks power. Here, we consider a particular type of nonclassical measurement error: skewed errors. Unfortunately, skewed measurement error is likely to be a relatively common feature of many outcomes of interest in political science research. This study highlights the bias that can result even from relatively “small” amounts of skewed measurement error, particularly, if the measurement error is heteroskedastic. We also assess potential solutions to this problem, focusing on the stochastic frontier model and Nonlinear Least Squares. Simulations and three replications highlight the importance of thinking carefully about skewed measurement error as well as appropriate solutions.

Download Full-text

A Comparison of Least Squares Linear Regression and Measurement Error Modeling of Warm / Cold Multipole Correlation in SSC Prototype Dipole Magnets

Supercollider 5 ◽

10.1007/978-1-4615-2439-7_168 ◽

1994 ◽

pp. 719-722 ◽

Cited By ~ 1

Author(s):

D. A. Pollock ◽

R. F. Gunst ◽

K. Kim ◽

W. R. Schucany

Keyword(s):

Measurement Error ◽

Linear Regression ◽

Least Squares ◽

Error Modeling

Download Full-text

Asymptotic Distribution of the Bias Corrected Least Squares Estimators in Measurement Error Linear Regression Models Under Long Memory

Journal of Time Series Analysis ◽

10.1111/jtsa.12451 ◽

2019 ◽

Vol 40 (4) ◽

pp. 493-518

Author(s):

Hira L. Koul ◽

Donatas Surgailis

Keyword(s):

Measurement Error ◽

Linear Regression ◽

Least Squares ◽

Asymptotic Distribution ◽

Long Memory ◽

Regression Models ◽

Linear Regression Models ◽

Least Squares Estimators

Download Full-text

Statistical modeling of the novel COVID-19 epidemic in Iraq

Epidemiologic Methods ◽

10.1515/em-2020-0025 ◽

2021 ◽

Vol 10 (s1) ◽

Author(s):

Ban Ghanim Al-Ani

Keyword(s):

Linear Regression ◽

Least Squares ◽

Nonlinear Regression ◽

Least Squares Method ◽

Growth Models ◽

Nonlinear Least Squares ◽

Weibull Model ◽

Simple Linear Regression ◽

Initial Values ◽

Nonlinear Least Squares Method

Abstract Objectives This study aimed to apply three of the most important nonlinear growth models (Gompertz, Richards, and Weibull) to study the daily cumulative number of COVID-19 cases in Iraq during the period from 13th of March, 2020 to 22nd of July, 2020. Methods Using the nonlinear least squares method, the three growth models were estimated in addition to calculating some related measures in this study using the “nonlinear regression” tool available in Minitab-17, and the initial values of the parameters were deduced from the transformation to the simple linear regression equation. Comparison of these models was made using some statistics (F-test, AIC, BIC, AICc and WIC). Results The results indicate that the Weibull model is the best adequate model for studying the cumulative daily number of COVID-19 cases in Iraq according to some criteria such as having the highest F and lowest values for RMSE, bias, MAE, AIC, BIC, AICc and WIC with no any violations of the assumptions for the model’s residuals (independent, normal distribution and homogeneity variance). The overall model test and tests of the estimated parameters showed that the Weibull model was statistically significant for describing the study data. Conclusions From the Weibull model predictions, the number of cumulative confirmed cases of novel coronavirus in Iraq will increase by a range of 101,396 (95% PI: 99,989 to 102,923) to 114,907 (95% PI: 112,251 to 117,566) in the next 24 days (23rd of July to 15th of August 15, 2020). From the inflection points in the Weibull curve, the peak date when the growth rate will be maximum, is 7th of July, 2020, and at this time the daily cumulative cases become 67,338. Using the nonlinear least squares method, the models were estimated and some related measures were calculated in this study using the “nonlinear regression” tool available in Minitab-17, and the initial values of the parameters were obtained from the transformation to the simple linear regression model.

Download Full-text