scholarly journals Finite-Sample Generalized Confidence Distributions and Sign-Based Robust Estimators In Median Regressions with Heterogeneous Dependent Errors

2017 ◽  
Author(s):  
Elise Coudin ◽  
Jean-Marie Dufour
Keyword(s):  
Robust Estimators ◽  
Finite Sample ◽  
Median Regressions

Large sample and finite sample behavior of robust estimators

2019 ◽  
pp. 173-199
Author(s):  
Jana Jurečková ◽  
Jan Picek ◽  
Martin Schindler
Keyword(s):  
Robust Estimators ◽  
Finite Sample ◽  
Large Sample

scholarly journals Causal inference for recurrent event data using pseudo-observations

Biostatistics ◽  
2020 ◽  
Author(s):  
Chien-Lin Su ◽  
Robert W Platt ◽  
Jean-François Plante
Keyword(s):  
Goodness Of Fit ◽  
Variance Estimation ◽  
Recurrent Event ◽  
Event Data ◽  
Robust Estimators ◽  
Recurrent Event Data ◽  
Finite Sample ◽  
Data Set ◽  
Asymptotically Normal ◽  
Doubly Robust

Summary Recurrent event data are commonly encountered in observational studies where each subject may experience a particular event repeatedly over time. In this article, we aim to compare cumulative rate functions (CRFs) of two groups when treatment assignment may depend on the unbalanced distribution of confounders. Several estimators based on pseudo-observations are proposed to adjust for the confounding effects, namely inverse probability of treatment weighting estimator, regression model-based estimators, and doubly robust estimators. The proposed marginal regression estimator and doubly robust estimators based on pseudo-observations are shown to be consistent and asymptotically normal. A bootstrap approach is proposed for the variance estimation of the proposed estimators. Model diagnostic plots of residuals are presented to assess the goodness-of-fit for the proposed regression models. A family of adjusted two-sample pseudo-score tests is proposed to compare two CRFs. Simulation studies are conducted to assess finite sample performance of the proposed method. The proposed technique is demonstrated through an application to a hospital readmission data set.

scholarly journals Some New Robust Estimators for Circular Logistic Regression Model with Applications on Meteorological and Ecological Data

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shokrya S. Alshqaq ◽  
Abdullah A. Ahmadini ◽  
Ali H. Abuzaid
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Likelihood Estimation ◽  
Simulation Studies ◽  
Robust Estimators ◽  
Finite Sample ◽  
Ecological Data ◽  
Logistic Regression Models

Maximum likelihood estimation ( MLE ) is often used to estimate the parameters of the circular logistic regression model due to its efficiency under a parametric model. However, evidence has shown that the classical MLE extremely affects the parameter estimation in the presence of outliers. This article discusses the effect of outliers on circular logistic regression and extends four robust estimators, namely, Mallows, Schweppe, Bianco and Yohai estimator BY , and weighted BY estimators, to the circular logistic regression model. These estimators have been successfully used in linear logistic regression models for the same purpose. The four proposed robust estimators are compared with the classical MLE through simulation studies. They demonstrate satisfactory finite sample performance in the presence of misclassified errors and leverage points. Meteorological and ecological datasets are analyzed for illustration.

Finite Sample Bias of Robust Estimators in Segmentation of Closely Spaced Structures: A Comparative Study

2010 ◽  
Vol 37 (1) ◽  
pp. 66-84 ◽  
Author(s):  
Reza Hoseinnezhad ◽  
Alireza Bab-Hadiashar ◽  
David Suter
Keyword(s):  
Comparative Study ◽  
Robust Estimators ◽  
Finite Sample ◽  
Sample Bias ◽  
Finite Sample Bias

From Finite Sample to Asymptotic Methods in Statistics

2009 ◽  
Author(s):  
Pranab K. Sen ◽  
Julio M. Singer ◽  
Antonio C. Pedroso de Lima
Keyword(s):  
Asymptotic Methods ◽  
Finite Sample
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