Randomization Does Not Justify Logistic Regression
نویسندگان
چکیده
منابع مشابه
Randomization Does Not Justify Logistic Regression
The logit model is often used to analyze experimental data. However, randomization does not justify the model, so the usual estimators can be inconsistent. A consistent estimator is proposed. Neyman’s non-parametric setup is used as a benchmark. In this setup, each subject has two potential responses, one if treated and the other if untreated; only one of the two responses can be observed. Besi...
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According to R.A. Fisher, randomization "relieves the experimenter from the anxiety of considering innumerable causes by which the data may be disturbed." Since, in particular, it is said to control for known and unknown nuisance factors that may considerably challenge the validity of a result, it has become very popular. This contribution challenges the received view. First, looking for quanti...
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An increase in quality and detail of publicly available databases increases the risk of disclosure of sensitive personal information contained in such databases. The goal of Statistical Disclosure Control (SDC) is to develop methodology that aims at minimizing disclosure risk while providing society with as much information as possible needed for valid statistical inference. The Post Randomizat...
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The common practice of collapsing inherently continuous or ordinal variables into two categories causes information loss that may potentially weaken power to detect effects of explanatory variables and result in Type II errors in statistical inference. The purpose of this investigation was to illustrate, using a substantive example, the potential increase in power gained from an ordinal instead...
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Logistic regression is a technique to map the input feature to the posterior probability for a binary class. The optimal parameter of regression function is obtained by maximizing log likelihood of training data. In this report, we implement two optimization techniques 1) stochastic gradient decent (SGD); 2) limited-memory BroydenFletcherGoldfarbShanno (L-BFGS) to optimize the log likelihood fu...
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ژورنال
عنوان ژورنال: Statistical Science
سال: 2008
ISSN: 0883-4237
DOI: 10.1214/08-sts262