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Empirical process of residuals for regression models with long memory errors
Empirical process of residuals regression models
2011/3/24
We consider the residual empirical process in random design regression with long memory errors. We establish its limiting behaviour, showing that its rates of convergence are different from the rates ...
From Sparse Signals to Sparse Residuals for Robust Sensing
Sparse Signals Sparse Residuals
2010/11/8
One of the key challenges in sensor networks is the extraction of information by fusing data from a multitude of distinct, but possibly unreliable sensors. Recovering information from the maximum numb...
Nonparametric kernel estimation of the probability density function of regression errors using estimated residuals
Kernel density estimation Leave-one-out kernel estimator Two-steps estimator
2010/10/14
This paper deals with the nonparametric density estimation of the regression error term assuming its independence with the covariate. The difference between the feasible estimator which uses the estim...
Residuals and goodness-of-fit tests for stationary marked Gibbs point processes
stationary marked Gibbs point processes residuals goodness-of-fit test quadrat count-ing test maximum pseudolikelihood estimator
2010/3/10
The inspection of residuals is a fundamental step to investigate the
quality of adjustment of a parametric model to data. For spatial
point processes, the concept of residuals has been recently prop...
Sequentially Updated Residuals and Detection of Stationary Errors in Polynomial Regression Models
Autoregressive unit root Change-point Control chart Nonparametric smoothing Sequential analysis Weighted partial sum process
2010/3/9
The question whether a time series behaves as a random walk or as a stationary
process is an important and delicate problem, particularly arising in financial statistics,
econometrics, and engineeri...
ASYMPTOTIC DISTRIBUTION OF UNBIASED LINEAR ESTIMATORS IN THE PRESENCE OF HEAVY-TAILED STOCHASTIC REGRESSORS AND RESIDUALS
Asymptotic distribution rate of convergence stochastic regressor stable non-Gaussian
2009/9/18
Under the symmetric a-stable distributional assumption
for the disturbances, Blattberg and Sargent [3] consider unbiased linear
estimators for a regression model with non-stochastic regressors.
We ...
A Comparison of Analysis of Covariate-Adjusted Residuals and Analysis of Covariance
allometry ANOVA clustering homogeneity of variances isometry Kruskal-Wallis test linearmodels parallel lines model
2010/3/19
Various methods to control the influence of a covariate on a response variable are compared. In particular,ANOVA with or without homogeneity of variances (HOV) of errors and Kruskal-Wallis (K-W) tests...