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Bias Correction for Fixed Effects Spatial Panel Data Models
Bootstrap Spatial Panel Individual Fixed Effects Time Fixed Effects
2016/1/26
This paper examines the finite sample properties of the quasi maximum likelihood (QML) esti-mators of the fixed effects spatial panel data (FE-SPD) models of Lee and Yu (2010). Following the general b...
Bias Correction for Fixed Effects Spatial Panel Data Models
Bootstrap Spatial Panel Individual Fixed Effects Time Fixed Effects
2016/1/20
This paper examines the finite sample properties of the quasi maximum likelihood (QML) esti-mators of the fixed effects spatial panel data (FE-SPD) models of Lee and Yu (2010). Following the general b...
Bias correction for estimators of the extremal index
absolute regularity clustering of extremes extremal index
2011/7/19
We investigate the joint asymptotic behavior of so-called blocks estimator of the extremal index, that determines the mean length of clusters of extremes, based on the exceedances over different thres...
Adjusting for selection bias in testing multiple families of hypotheses
false discovery rate family-wise error rate hierarchical testing
2011/7/6
In many large multiple testing problems the hypotheses are divided into families. Given the data, families with evidence for true discoveries are selected, and hypotheses within them are tested.
Uniform bias study and Bahadur representation for local polynomial estimators of the conditional quantile function
Bahadur representation Conditional quantile function Local polynomial estimation Econometrics of Auctions
2011/6/20
This paper investigates the bias and the weak Bahadur representation of a local
polynomial estimator of the conditional quantile function and its derivatives.
The bias and Bahadur remainder term are...
Iterative bias reduction multivariate smoothing in R: The ibr package
multivariate smoothing L2 boosting thin-plate splines kernel regression R
2011/6/20
In multivariate nonparametric analysis, sparseness of the co-
variates also called curse of dimensionality, forces one to use large smoothing
parameters. This leads to a biased smoother. Instead of ...
Recursive bias estimation for multivariate regression smoothers
nonparametric regression；smoother；kernel；thin-plate splines；stopping rules
2011/6/17
This paper presents a practical and simple fully nonparametric multivariate smooth-
ing procedure that adapts to the underlying smoothness of the true regression function. Our
estimator is easily co...
The Importance of Scale for Spatial-Confounding Bias and Precision of Spatial Regression Estimators
Epidemiology, identifiability, mixed model,penalized likelihood random effects spatial correlation splines
2010/11/9
Residuals in regression models are often spatially correlated.Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants.
A Generalized Publication Bias Model
publication bias meta-analysis file-drawer hypothesis fail-safe number
2010/4/30
Scargle (2000) has discussed Rosenthal&Rubin's (1978) "fail-safe number" (FSN)
method for estimating the number of unpublished studies in meta-analysis. He concluded
that this FSN cannot possibly be...
Does adjustment for measurement error induce positive bias if there is no true association?
adjustment measurement error positive bias true association
2010/3/18
This article is a response to an off-the-record discussion that I had at an international meeting of epidemiologists. It centered on a concern, perhaps widely spread, that measurement error
adjustmen...
Relative Age Effect in Elite Sports:Methodological Bias or Real Discrimination?
Relative Age Effect Soccer Discrimination Bias
2010/3/9
Sport sciences researchers talk about a relative age effect when they observe a biased
distribution of elite athletes’ birthdates, with an over-representation of those born at the
beginning of the c...
Relaxation Penalties and Priors for Plausible Modeling of Nonidentified Bias Sources
Bias biostatistics causality epidemiology measurement error misclassification observational studies odds ratio relative risk
2010/3/9
In designed experiments and surveys, known laws or de-
sign feat ures provide checks on the most relevant aspects of a model
and identify the target parameters. In contrast, in most observational
s...
On minimum bias and variance estimation for parametric models with shrinking contamination
minimum bias and variance estimation parametric models shrinking contamination
2009/9/24
A close relationship is derived between optimal Mestimation
and optimal robust testing for shrinking contaminations.
Explicit formulas are given for solutions when the loss is defined as
convex com...
The papex deals with the concept of robustness given
by Zielidski (see [17J and [18]). The uniformly most bias-robust
estimates of the scale parameter, based on order statistics and
spacings, for s...
A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models
feature selection optimistic bias naive Bayes models gene expression data
2009/9/22
For many classication and regression problems, a large number of
features are available for possible use this is typical of DNA microarray data
on gene expression, for example. Often, for computatio...