In precision agriculture regression has been used widely to quantify the relationship between soil attributes and other environmental variables. However, spatial correlation existing in soil samples usually violates a basic assumption of regression: sample independence. In this study, a regression-kriging method was attempted in relating soil properties to the remote sensing image of a cotton f...

Residuals have long been used as the basis for graphical and numerical examination of the adequacy of regression models. Conventional residual analysis based on plotting raw residuals or their smoothed versions is highly subjective, whereas most goodness-offit tests provide little information about the nature of model inadequacy. In this paper, new model-checking techniques of Lin et al. (1993,...

One aspect of vector-space semantic similarity estimates which has so far received little attention is their dependence on the length of the texts to be compared. A simple experiment will demonstrate this effect. The data set used for this demonstration involves texts from the Lexile collection, a set of general fiction titles spanning a wide range of grade-school reading levels, which ETS lice...

An essential assumption in estimating the source parameters in electromagnetic source analysis (EMSA) is that the number of sources is known. If the incorrect number of sources is assumed in the estimation procedure, errors could occur [1]. Supek and Aine [2], for example, showed that if too many dipoles are assumed (over-modeling) then ‘spurious’ dipoles could arise with high standard errors, ...

Making informed decisions about model adequacy has been an outstanding issue for regression models with discrete outcomes. Standard assessment tools such outcomes (e.g., deviance residuals) often show a large discrepancy from the hypothesized pattern even under true and are not informative, especially when data highly binary). To fill this gap, we propose quasi-empirical residual distribution f...

This paper presents a goodness-of-fit test for the logistic regression model under case-control sampling. The test statistic is constructed via a discrepancy between two competing kernel density estimators of the underlying conditional distributions given case-control status. The proposed goodness-of-fit test is shown to compare very favorably with previously proposed tests for case-control sam...

Atypical observations hidden in the data may play quite an disastrous role in a tted regression, especially when commonly used outlier detection techniques like computing leverages, Mahalanobis distances, ordinary and studentized residuals, DFFits, cross-validations { do not detect them. However (multivariate) outliers can be detected quite easily by graphical techniques , e.g. scatterplot matr...

Jackknife-after-Bootstrap (JaB) has first been proposed by [1] then used by [2] and [3] to detect influential observations in linear regression models. In this study, we propose using JaB to detect influential observations in logistic regression model. Performance of the proposed method will be compared with the traditional method for standardized Pearson residuals, Cook’s distance, change in t...

Abstract. For the given data (wi, xi, yi), i = 1, . . . , n, and the given model function f(x;θ), where θ is a vector of unknown parameters, the goal of regression analysis is to obtain estimator θ∗ of the unknown parameters θ such that the vector of residuals is minimized in some sense. The common approach to this problem of minimization is the least-squares method, that is minimizing the L2 n...

We analyze tidal data from Port Mansseld, TX using Kunsch's (1989) blockwise bootstrap in the regression setting. In particular, we estimate the variability of parameter estimates in a harmonic analysis via block subsampling of residuals from a least squares t. We see that naive least squares variance estimates can be either too large or too small depending on the strength of correlation and th...