A genetic algorithm (GA), an optimization procedure based on the theory of evolution, was compared with nonlinear regression for the ability of the 2 algorithms to fit the coefficients of poultry growth models. It was hypothesized that the nonlinear approach of using GA to define the parameters of growth equations would better fit the growth equations than the use of nonlinear regression. Two s...

Random coefficient regression models have received considerable attention, especially from econometricians. Previous work has assumed that the coefficients have normal distributions. The variances of the coefficients have, in previous papers, been estimated by maximum likelihood or by least squares methodology applied to the squared residuals from a preliminary (unweighted) fit. Maximum likelih...

In this paper, the main idea is to compute the robust regression model, derived by experimentation, in order to achieve a model with minimum effects of outliers and fixed variation among different experimental runs. Both outliers and nonequality of residual variation can affect the response surface parameter estimation. The common way to estimate the regression model coefficients is the ordinar...

An L2-boosting algorithm for estimation of a regression function from random design is presented, which consists of fitting repeatedly a function from a fixed nonlinear function space to the residuals of the data by least squares and by defining the estimate as a linear combination of the resulting least squares estimates. Splitting of the sample is used to decide after how many iterations of s...

The intermethod variabilities of control materials and patient blood samples for the measurement of glycohemoglobin were compared. Sets of 50 blood samples and 15 control materials were analyzed by HPLC and affinity and immunochemical methods. For each pair of methods, the distances of the materials from the regression line of patient blood results (expressed as normalized residuals) were calcu...

This article describes a new software for modeling correlated binary data based on orthogonalized residuals, a recently developed estimating equations approach that includes, as a special case, alternating logistic regressions. The software is flexible with respect to fitting in that the user can choose estimating equations for association models based on alternating logistic regressions or ort...

This paper presents a simple two-step nonparametric estimator for a triangular simultaneous equation model. Our approach employs series approximations that exploit the additive structure of the model. The first step comprises the nonparametric estimation of the reduced form and the corresponding residuals. The second step is the estimation of the primary equation via nonparametric regression wi...

This paper investigates the relationship between consumer credits and interest rates in Turkey based on the fractional cointegration approach by using daily observations over the period from 4 January 2002 to 24 December 2010. First, we ignore the possible structural breaks in the series and perform the Geweke and Porter-Hudak (GPH) test on the residuals for fractional cointegration. Second, we...

Parsimonious regression models using locational data often yield non-normal, heteroskedastic, and spatially dependent residuals. This manuscript develops a model which simultaneously performs spatial and functional form transformations to mitigate these problems. We apply the model to 11,006 observations on housing prices in Baton Rouge. For these data, the model reduces the interquartile range...

In a regression model with time series data, whether the regressors are cointegrated or not, the structure of X’X is usually characterized by multicollinearity. An additive model is postulated and estimated via the backfitting algorithm. The model sequentially smooth the residuals by entering one variable-at-a-time into the equation until the residuals behave randomly or all the effects of the ...