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Logistic Regression in Rare Events Data
, 1999
"... We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a ..."
Abstract

Cited by 115 (4 self)
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We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quartermillion dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99 % of their (nonfixed) data collection costs or to collect much more meaningful explanatory
Explaining Rare Events in International Relations
, 2000
"... Some of the most important phenomena in international conflict are coded as "rare events data," binary dependent variables with dozens to thousands of times fewer events, such as wars, coups, etc., than "nonevents". Unfortunately, rare events data are difficult to explain and pre ..."
Abstract

Cited by 14 (2 self)
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Some of the most important phenomena in international conflict are coded as "rare events data," binary dependent variables with dozens to thousands of times fewer events, such as wars, coups, etc., than "nonevents". Unfortunately, rare events data are difficult to explain and predict, a problem that seems to have at least two sources. First, and most importantly, the data collection strategies used in international conflict are grossly inefficient. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99% of their (nonfixed) data collection costs, or to collect much more meaningful explanatory variables. Second, logistic regression, and other commonly ...
and
, 2010
"... A general iterative algorithm is developed for the computation of reducedbias parameter estimates in regular statistical models using the adjusted score approach of Firth (1993, Biometrika 80, 27–38). The algorithm unifies and provides appealing new interpretation for iterative methods that have be ..."
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A general iterative algorithm is developed for the computation of reducedbias parameter estimates in regular statistical models using the adjusted score approach of Firth (1993, Biometrika 80, 27–38). The algorithm unifies and provides appealing new interpretation for iterative methods that have been published previously for some specific model classes. The new algorithm can usefully be viewed as a series of iterative bias corrections, thus facilitating the adjusted score approach to bias reduction in any model for which the firstorder bias of the maximum likelihood estimator has already been derived. The method is tested by application to a logitlinear multiple regression model with betadistributed responses; the results confirm the effectiveness of the new algorithm, and also reveal some important errors in the existing literature on beta regression.
Translated by Kimon FriarContents
"... Discipline is the highest of all virtues. Only so may strength and desire be counterbalanced and the endeavors of man bear fruit. N. KAZANTZAKIS, ..."
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Discipline is the highest of all virtues. Only so may strength and desire be counterbalanced and the endeavors of man bear fruit. N. KAZANTZAKIS,
Section of Clinical Biometrics Fax: (+43)(1) 40400/6687
"... A SAS macro, SPLUS library and R package to perform logistic regression without convergence problems ..."
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A SAS macro, SPLUS library and R package to perform logistic regression without convergence problems
www.mdpi.com/journal/ijerph Pilot Study of Pesticide Knowledge, Attitudes, and Practices among Pregnant Women in Northern Thailand
, 2012
"... Abstract: An estimated 200,000 children born in Thailand each year are at risk of prenatal exposure to pesticides and associated neurodevelopmental outcomes because of their mothers ’ agricultural occupations. Children born to nonagricultural workers may also be at risk of exposure from other pathw ..."
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Abstract: An estimated 200,000 children born in Thailand each year are at risk of prenatal exposure to pesticides and associated neurodevelopmental outcomes because of their mothers ’ agricultural occupations. Children born to nonagricultural workers may also be at risk of exposure from other pathways of maternal pesticide exposure, including exposure through home use, diet, and other environmental media. Pesticide exposure in Thailand has been linked to unsafe practices and beliefs about pesticides. However, limited information exists on pesticide knowledge, attitudes, and practices among pregnant women in Thailand or elsewhere. Obtaining this information is essential to understand the factors associated with prenatal pesticide exposure, identify populations potentially at risk, and ultimately protect pregnant women and their children. We administered surveys to 76 pregnant women in northern Thailand and used multivariable logistic regression to evaluate associations among pesticiderelated knowledge, pregnancy trimester, and pesticide use behavior. In this pilot study, lower knowledge score and earliest trimester of pregnancy were marginally (p < 0.1) associated with unsafe practices in the home, but not at work. Women who worked in agriculture or applied pesticides before becoming pregnant, or who
WITH MISSING DATA by
"... The receiver operating characteristic (ROC) curve methodology is the statistical methodology for assessment of the accuracy of diagnostics tests or biomarkers. Currently most widely used statistical methods for the inferences of ROC curves are completedata based parametric, semiparametric or nonp ..."
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The receiver operating characteristic (ROC) curve methodology is the statistical methodology for assessment of the accuracy of diagnostics tests or biomarkers. Currently most widely used statistical methods for the inferences of ROC curves are completedata based parametric, semiparametric or nonparametric methods. However, these methods cannot be used in diagnostic applications with missing data. In practical situations, missing diagnostic data occur more commonly due to various reasons such as medical tests being tooexpensive, too time consuming or too invasive. This dissertation aims to develop new nonparametric statistical methods for evaluating the accuracy of diagnostic tests or biomarkers in the presence of missing data. Specifically, novel nonparametric statistical methods will be developed with different types of missing data for (i) the inference of the area under the ROC curve (AUC, which is a summary index for the diagnostic accuracy of the test) and (ii) the joint inference of the sensitivity and the specificity of a continuousscale diagnostic test. In this dissertation, we will provide a general framework that combines the empirical likelihood and general estimation equations with nuisance parameters for the joint inferences of sensitivity
and
"... This paper is made available online in accordance with publisher policies. Please scroll down to view the document itself. Please refer to the repository record for this item and our policy information available from the repository home page for further information. To see the final version of this ..."
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This paper is made available online in accordance with publisher policies. Please scroll down to view the document itself. Please refer to the repository record for this item and our policy information available from the repository home page for further information. To see the final version of this paper please visit the publisher’s website. Access to the published version may require a subscription. Author(s): Ioannis Kosmidis and David Firth Article Title: A generic algorithm for reducing bias in parametric estimation
SUMMARY
"... Smallsample bias and corrections for conditional maximumlikelihood oddsratio estimators ..."
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Smallsample bias and corrections for conditional maximumlikelihood oddsratio estimators