The sensitivity of the least-squares estimation in a regression model is impacted by multicollinearity and autocorrelation problems. To deal with multicollinearity, Ridge, Liu, Ridge-type biased estimators have been presented statistical literature. recently proposed Kibria-Lukman estimator one estimators. literature has compared others using mean square error criterion for linear model. It was...

This paper tries ®rst to introduce and motivate the methodology of multivariate calibration. Next a review is given, mostly avoiding technicalities, of the somewhat messy theory of the subject. Two approaches are distinguished: the estimation approach (controlled calibration) and the prediction approach (natural calibration). Among problems discussed are the choice of estimator, the choice of c...

We propose an optimum mechanism for providing monetary incentives to the data sources of a statistical estimator such as linear regression, so that high quality data is provided at low cost, in the sense that the weighted sum of payments and estimation error is minimized. The mechanism applies to a broad range of estimators, including linear and polynomial regression, kernel regression, and, un...

In this paper, a modified estimation algorithm has been developed refers to Covariance Shaping Least Square (CSLS) estimation based on the quantum mechanical concepts and constraints. The algorithm has been applied to Auto Regressive Moving Average (ARMA models with various parameter values. The same models can be applied with Colored Noise which estimates the bias in the parameter and the vali...

The purpose of this study was to follow Acree’s 1999 theory and demonstrate that there are significant differences in the loop ridge count of male subjects compared to that of female subjects. This was based on the belief that women tend to have finer ridge detail, therefore more ridges, while men have coarse ridge detail, thereby fewer ridges [1]. This study, in contrast to Acree’s methodology...

Orthonormal ridgelets are a specialized set of angularly-integrated ridge functions which make up an orthonormal basis for L2(R). In this paper we explore the relationship between orthonormal ridgelets and true ridge functions r(x1 cos θ + x2 sin θ). We derive a formula giving the ridgelet coefficients of a ridge function in terms of the 1-D wavelet coefficients of the ridge profile r(t), and w...

Cotton (Gossypium hirsutum L.) seeds are susceptible to low temperature and excess moisture in soil during seed emergence in years with high rainfall and low temperature in spring. Therefore, a two-year field experiment was carried out to evaluate effects of ridge tillage formed in autumn (RT-I), ridge tillage formed about a month before planting (RT-II) and flat conventional tillage (CT) culti...

There is an increasing demand for automated detection and analysis of dermoscopy structures and malignancy clues such as streaks in dermoscopy images, for computer-aided early diagnosis of deadly melanoma. This paper presents a novel approach for streak detection and visualization on dermoscopic images. We tackle the detection of streaks by means of ridge and valley estimation. Orientation esti...

Quantitative portfolio allocation requires the accurate and tractable estimation of covariances between a large number of assets, whose histories can greatly vary in length. Such data are said to follow a monotone missingness pattern, under which the likelihood has a convenient factorization. Upon further assuming that asset returns are multivariate normally distributed, with histories at least...

This paper attempts to solve a basic problem in distributed statistical inference: how many machines can we use in parallel computing? In kernel ridge regression, we address this question in two important settings: nonparametric estimation and hypothesis testing. Specifically, we find a range for the number of machines under which optimal estimation/testing is achievable. The employed empirical...