Residual-based analysis is generally considered a cornerstone of statistical methodology. For a special case of indirect regression, we investigate the residual-based empirical distribution function and provide a uniform expansion of this estimator, which is also shown to be asymptotically most precise. This investigation naturally leads to a completely data-driven technique for selecting a reg...

A combination of conceptual analysis and empirical analysis-partial regression and residuals analysis-was used to derive an appropriate functional form hedonic price model. These procedures are illustrated in the derivation of a functional form hedonic model for an automated, econometric daily cotton price reporting system for the Texas-Oklahoma cotton market. Following conceptualization to ded...

The flexibility of PLS algorithm can be used to assign suitable weights to predictors or to objects or to both predictors and objects. Weights of predictors are obtained from the regression coefficients and the standard deviation. Weights of objects are obtained from the prediction residuals. By iterative weighting, the regression models are refined and a steady state is attained, where useless...

A simple constructive learning algorithm is proposed for regime discovery in non-stationary time series. The approach is based on competition among regression models aided by averaging of their squared residuals over neighboring data points and incremental introduction of additional models when needed. A case study on California’s deregulated power market discovered that 4 regimes existed in ma...

This paper reports on an extensive Monte Carlo study of seven residual-based tests of the hypothesis of no cointegration. Critical values and the power of the tests under the alternative of fractional cointegration are simulated and compared. It turns out that the Phillips-Perron t-test when applied to regression residuals is more powerful than Geweke-Porter-Hudak tests and the Augmented Dickey...

Fuzzy linear regression is an interesting tool for handling uncertain data samples as an alternative to a probabilistic approach. This paper sets forth uses a linear regression model for fuzzy variables; the model is optimized through convex methods. A fuzzy linear programming model has been designed to solve the problem with nonlinear fuzzy data by combining the fuzzy arithmetic theory with co...

Data envelopment analysis (DEA) is an axiomatic, mathematical programming approach to productive efficiency analysis and performance measurement. This paper shows that DEA can be interpreted as a nonparametric least squares regression subject to shape constraints on production frontier and sign constraints on residuals. Thus, DEA can be seen as a nonparametric counter-part of the corrected ordi...

Case-based reasoning (CBR) systems use similarity functions to solve new problems with past situations. K-nearest neighbors algorithm (K-NN) have been used in CBR systems to define new cases status according to characteristics of past nearest cases. We proposed a new hybrid approach combining logistic regression (LR) with K-NN to optimize CBR classification. First, we analyzed the knowledge dat...

Consider a linear regression model subject to an error distribution which is symmetric about 0 and varies regularly at 0 with exponent ζ . We propose two estimators of ζ , which characterizes the central shape of the error distribution. Both methods are motivated by the well-known Hill estimator, which has been extensively studied in the related problem of estimating tail indices, but substitut...

In simple linear regression the determination coefficient tells us which percentage of the variance of the response variable is explained by the fitted linear mapping of the explanatory variable. In this paper we examine how to extend the notion of determination coefficient to mean absolute value and least median of squares regression. The concept behind our proposals for the extension originat...