Inference with Large Clustered Datasets

نویسندگان

چکیده

برای دانلود رایگان 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

Robust Inference with Clustered Data

In this paper we survey methods to control for regression model error that is correlated within groups or clusters, but is uncorrelated across groups or clusters. Then failure to control for the clustering can lead to understatement of standard errors and overstatement of statistical signi cance, as emphasized most notably in empirical studies by Moulton (1990) and Bertrand, Du o and Mullainath...

متن کامل

Inference for Clustered Data

This article introduces clusteff, a new Stata command for checking the severity of cluster heterogeneity in cluster robust analyses. Cluster heterogeneity can cause a size distortion leading to underrejection of the null hypothesis. Carter, Schnepel, and Steigerwald (2015) develop the effective number of clusters to reflect a reduction in the degrees of freedom, thereby mirroring the distortion...

متن کامل

Robust Inference with Clustered Data A

In this paper we survey methods to control for regression model error that is correlated within groups or clusters, but is uncorrelated across groups or clusters. Then failure to control for the clustering can lead to understatement of standard errors and overstatement of statistical signi cance, as emphasized most notably in empirical studies by Moulton (1990) and Bertrand, Du o and Mullainath...

متن کامل

Sparse Density Representations for Simultaneous Inference on Large Spatial Datasets

Large spatial datasets often represent a number of spatial point processes generated by distinct entities or classes of events. When crossed with covariates, such as discrete time buckets, this can quickly result in a data set with millions of individual density estimates. Applications that require simultaneous access to a substantial subset of these estimates become resource constrained when d...

متن کامل

An Adaptive Subsampling Approach for MCMC Inference in Large Datasets

Markov chain Monte Carlo (MCMC) methods are often deemed far too computationally intensive to be of any practical use for large datasets. This paper describes a methodology that aims to scale up the Metropolis-Hastings (MH) algorithm in this context. We propose an approximate implementation of the accept/reject step of MH that only requires evaluating the likelihood of a random subset of the da...

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: L'Actualité économique

سال: 2017

ISSN: 1710-3991,0001-771X

DOI: 10.7202/1040501ar