Inference on distribution functions under measurement error

نویسندگان
چکیده

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Sensitivity analysis for causal inference under unmeasured confounding and measurement error problems.

In this article, we present a sensitivity analysis for drawing inferences about parameters that are not estimable from observed data without additional assumptions. We present the methodology using two different examples: a causal parameter that is not identifiable due to violations of the randomization assumption, and a parameter that is not estimable in the nonparametric model due to measurem...

متن کامل

Nonparametric Bayes Inference for Concave Distribution Functions

A way of making Bayesian inference for concave distribution functions is introduced. This is done by uniquely transforming a mixture of Dirichlet processes on the space of distribution functions to the space of concave distribution functions. The approach also gives a way of making Bayesian analysis of mul-tiplicatively censored data. We give a method for sampling from the posterior distributio...

متن کامل

Measurement error models with a general class of error distribution

The ordinary maximum likelihood (ML) approach in classical regression models, fails when the independent variables are subject to error. The most noticeable and well known problem reported in the literature is the inconsistency of the ML estimators [1]. To solve this problem, a number of alternatives were proposed. The measurement error model (MEM) is the most fashionable of them, but it has so...

متن کامل

Empirical likelihood inference in the presence of measurement error

Suppose that several different imperfect instruments and one perfect instrument are used independently to measure some characteristic of a population. The authors consider the problem of combining this information to make statistical inference on parameters of interest, in particular the population mean and cumulative distribution function. They develop maximum empirical likelihood estimators a...

متن کامل

Likelihood inference in generalized linear mixed measurement error models

The generalized linear mixed models (GLMMs) for clustered data are studied when covariates aremeasured with error. Themost conventional measurement error models are based on either linear mixed models (LMMs) or GLMMs. Even without the measurement error, the frequentist analysis of LMM, and particularly of GLMM, is computationally difficult. On the other hand, Bayesian analysis of LMM and GLMM i...

متن کامل

ذخیره 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: Journal of Econometrics

سال: 2020

ISSN: 0304-4076

DOI: 10.1016/j.jeconom.2019.09.002