Journal ArticleDOI
Regression problems with controllable variables subject to error
Reads0
Chats0
TLDR
In this article, the problem of estimating parameters when controllable variables are subject to errors is studied and strongly consistent estimators with an asymptotically normal distribution are proposed.Abstract:
SUMMARY The problem is studied of estimating parameters when controllable variables are subject to errors. Strongly consistent estimators with an asymptotically normal distribution are proposed and an iterative procedure for their calculation is suggested. A numerical example is given in illustration.read more
Citations
More filters
Journal ArticleDOI
Heteroscedastic nonlinear regression
S. L. Beal,L. B. Sheiner +1 more
TL;DR: In this article, several parameter estimation methods for dealing with heteroscedasticity in nonlinear regression are described, including variations on ordinary, weighted, iteratively reweighted, extended and generalized least squares.
Journal ArticleDOI
Sensitivity, bias, and theory in impact evaluations.
Robert F. Boruch,Hernando Gomez +1 more
Journal ArticleDOI
Variogram Fitting by Generalized Least Squares Using an Explicit Formula for the Covariance Structure
TL;DR: In this article, a generalized least square method with an explicit formula for the covariance structure (GLSE) was proposed to fit the variogram in the context of spatial statistics.
Journal ArticleDOI
An algorithm for the optimum distribution of a regional seismic network — II. An analysis of the accuracy of location of local earthquakes depending on the number of seismic stations
TL;DR: In this article, the authors compared seven optimal networks consisting of 4 to 10 stations for a given region, where velocity-depth profiles and the distribution of seismic intensity are known, and the application of optimum methods to the planning of seismic networks in the Lublin Coal Basin is presented.
Book ChapterDOI
Maximum likelihood estimation in a latent variable problem
TL;DR: This chapter discusses the maximum likelihood estimation in a latent variable problem, which is a viable approach to a broad class of latent variable problems.
References
More filters
Journal ArticleDOI
Are there Two Regressions
TL;DR: In this paper, a line is fitted by least squares, minimizing the sum of the squared residuals of the dependent uncontrolled variate, regardless of whether the independent variate has been measured without error as v or with error as y.
Journal ArticleDOI
Data Uncertainties and Least Squares Regression
S. D. Hodges,P. G. Moore +1 more
TL;DR: In this paper, the effect of errors in the independent variables is examined and some practical guidelines for the user of least squares are suggested. And the implications for forecasting are also examined, and simple methods are derived for assessing the sensitivity of the regression coefficients to each observation, and for calculating the approximate amount of bias in the estimated coefficients.
Journal ArticleDOI
Likelihood Distributions for Estimating Functions when Both Variables are Subject to Error
TL;DR: In this article, the problem of least square equations with variable weights is solved by an iterative procedure, where the unknown true values of the independent variable are eliminated from the likelihood to give a new likelihood which resembles a normal distribution with variances which depend on the unknown function.