Inverse probability weighted estimation for general missing data problems

Inverse-Probability-Weighted Estimation for Monotone and Nonmonotone Missing Data.

Missing data is a common occurrence in epidemiologic research. In this paper, 3 data sets with induced missing values from the Collaborative Perinatal Project, a multisite US study conducted from 1959 to 1974, are provided as examples of prototypical epidemiologic studies with missing data. Our goal was to estimate the association of maternal smoking behavior with spontaneous abortion while adj...

Inverse probability weighted estimation in survival analysis

Modern epidemiologic and clinical studies aimed at analyzing a time to an event endpoint T routinely collect, in addition to (possibly censored) information on T, high dimensional data often in the form of baseline (i.e. timeindependent covariates V (0)) and time-varying covariates V (t) , t > 0, measured at frequent intervals. Scientific interest, however, often focuses on a low dimensional fu...

Weighted Least Squares Estimation with Missing Data

Performance evaluation of different estimation methods for missing rainfall data

There are numerous methods to estimate missing values of which some are used depending on the data type and regional climatic characteristics. In this research, part of the monthly precipitation data in Sarab synoptic station, east Azerbaijan province, Iran was randomly considered missing values. In order to study the effectiveness of various methods to estimate missing data, by seven classic s...

Inverse DEA Model with Fuzzy Data for Output Estimation

In this paper, we show that inverse Data Envelopment Analysis (DEA) models can be used to estimate output with fuzzy data for a Decision Making Unit (DMU) when some or all inputs are increased and deficiency level of the unit remains unchanged.