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Alternative method for choosing ridge parameter for regression
Ridge regression Ridge parameter Multicollinearity
2010/9/20
The parameter estimation method based on minimum residual sum of squares is unsatisfactory in the presence of multicollinearity. Hoerl and Kennard [1] introduced alternative method called ridge regres...
An analytic comparison of permutation methods for tests of partial regression coefficients in the linear model
analytic comparison permutation methods linear model
2010/9/21
An analytic comparison of permutation methods for tests of partial regression coefficients in the linear model.
On estimating parameters of censored generalized Poisson regression model
Generalized Poisson regression Mixture model
2010/9/21
When the sampling variance of a count variable Y is significantly greater or less than that predicted by an expected probability distribution, Y is said to be over- or underdispersed, respectively. A ...
Regression, model misspecification and causation, with pedagogical demonstration
Regression variable omission model incomplete bias
2010/9/20
This paper shows, by a proposition and a numerical example, how a classic simple or multiple normal regression can achieve with 0.99 probability a near perfect fit to a random sample of any size but d...
A comparison of various influential points diagnostic methods and robust regression approaches: Reanalysis of interstitial lung disease data
robust regression methods local influence likelihood displacement
2010/9/25
In a linear regression model, the estimation of regression parameters by ordinary least squares method is affected by some anomalous points in the data set. Thus, detection of these abnormal points is...
A Monte Carlo comparison between ridge and principal components regression methods
Multicollinearity ridge regression principal component regression
2010/9/15
A basic assumption concerned with general linear regression model is that there is no correlation (or no multicollinearity) between the explanatory variables. When this assumption is not satisfied, th...
An effect of inflation illustrated by introducing a regression technique
linearized regression credibility premium the structural parameters unbiased estimators
2009/1/6
The idea of considering regression credibility models originated
from Hachemeister. He was confronted with claim ¯gures the di®erent
states of the USA. It was obvious from the ¯gures t...
Strong Convergence Rates of Wavelet Estimators in Semiparametric Regression Models with Censored Data
Semiparametric regression model Wavelet estimate Censored data Law of the iterated logarithm Strong uniform convergence rate
2008/11/10
The paper studies a semiparametric regression model
Yi Xi = Xiβ+gT +ei, i = 1,2,L,n .
where Yi is censored on the right by another random variable Ci with known or unknown distribution G . First...
Fuzzy linear regression models with fuzzy entropy
Fuzzy numbers Fuzzy linear regression Fuzzy entropy
2010/9/15
Fuzzy regression analysis using fuzzy linear models with symmetric triangular fuzzy number coefficient has been introduced by Tanaka et al.The goal of this regression is to find the coefficient of a p...
Exact distributions for sensitivity analysis in linear regression
Diagnostic outlier Cook’s distance elliptical law
2010/9/15
Based on a multivariate linear regression model, we propose several generalizations to the multivariate classical and modified Cook’s distances in order to detect one or more influential observations ...
Orthogonal linear regression in Roentgen stereophotogrammetry
mathematical photogrammetry projective geometry
2010/9/15
Rooted in aerial reconnaissance, mathematical photogrammetry has evolved into a mainstay of biomedical image processing. The present paper develops an algorithm for Roentgen stereophotogrammetry, a me...
Consider the partly linear regression model y_i = x'_iβ + g(t_i) + ε_i, 1 ≤ i ≤ n, where y_i's are responses, x_i = (x_i1,x_i2,…,x_ip)＇ and t_i ∈ Τ are known and nonrandom design Τ is a compact set in...
Coverage Accuracy of Confidence Intervals in Nonparametric Regression
confidence interval empirical likelihood
2007/12/11
Point-wise confidence intervals for a nonparametric regression function with random design points are considered. The confidence intervals are those based on the traditional normal approximation and t...
Spatial Nonparametric Regression Estimation: Non-isotropic Case
bandwidth kernel estimator mixing non-isotropic
2007/12/11
Data collected on the surface of the earth often has spatial interaction. In this paper, a non-isotropic mixing spatial data process is introduced, and under such a spatial structure a nonparametric k...
Delete-group Jackknife Estimate in Partially Linear Regression Models with Heteroscedasticity
partially linear regression model asymptotic variance
2007/12/10
Consider a partially linear regression model with an unknown vector parameter β, an unknown function g(·), and unknown heteroscedastic error variances. Chen, You~([23]) proposed a semiparametric gener...