Score and Pseudo-Score Confidence Intervals for Categorical Data Analysis
نویسندگان
چکیده
منابع مشابه
Score and Pseudo-Score Confidence Intervals for Categorical Data Analysis
This article reviews methods for constructing confidence intervals for analyzing categorical data. A considerable literature indicates that the method of inverting score tests performs well for a variety of cases. When the sample size is small or the parameter is near the parameter space boundary, this method usually performs much better than inverting Wald tests and sometimes better than inver...
متن کاملScore distributions for Pseudo Relevance Feedback
Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the Pseudo Relevance Feedback task. It is known that one of the factors that more affect to the Pseudo Relevance Fee...
متن کاملBootstrap Confidence Intervals for the Estimation of Average Treatment Effect on Propensity Score
Funded by collage st. innovative projects Received: January 21, 2011 Accepted: February 10, 2011 doi:10.5539/jmr.v3n3p52 Abstract Causal inferences on the average treatment effect in observational studies are always difficult problems because the distributions of samples in the two treatment groups can not be observed at the same time, and the estimation of the treatment effect is often biased....
متن کاملPropensity score based data analysis
For some time, propensity score (PS) based methods have been frequently applied in the analysis of observational and registry data. The PS is the conditional probability of a certain treatment given patient’s covariates. PS methods are used to eliminate imbalances in baseline covariate distributions between treatment groups and permit to estimate marginal effects. The package nonrandom is a too...
متن کاملBayesian propensity score analysis for observational data.
In the analysis of observational data, stratifying patients on the estimated propensity scores reduces confounding from measured variables. Confidence intervals for the treatment effect are typically calculated without acknowledging uncertainty in the estimated propensity scores, and intuitively this may yield inferences, which are falsely precise. In this paper, we describe a Bayesian method t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistics in Biopharmaceutical Research
سال: 2011
ISSN: 1946-6315
DOI: 10.1198/sbr.2010.09053