Pseudo Empirical Likelihood Confidence Intervals for Complex Sample Survey Data (original) (raw)

A Closer Examination of Subpopulation Analysis of Complex-Sample Survey Data

The Stata Journal: Promoting communications on statistics and Stata, 2008

In recent years, general-purpose statistical software packages have incorporated new procedures that feature several useful options for design-based analysis of complex-sample survey data. A common and frequently desired technique for analysis of survey data in practice is the restriction of estimation to a subpopulation of interest. These subpopulations are often referred to interchangeably in a variety of fields as subclasses, subgroups, and domains. In this article, we consider two approaches that analysts of complex-sample survey data can follow when analyzing subpopulations; we also consider the implications of each approach for estimation and inference. We then present examples of both approaches, using selected procedures in Stata to analyze data from the National Hospital Ambulatory Medical Care Survey (NHAMCS). We conclude with important considerations for subpopulation analyses and a summary of suggestions for practice.

Some Issues in the Analysis of Complex Survey Data

2002

We are concerned about inference on a parameter of a stochastic model with an estimator using data from a complex sample. Classical sampling theory concerns inferences for finite population parameters. H~jek (1960), Krewski and Rao (1981), Binder (1983) and others, studied and obtained results on the asymptotic properties of the sample estimator under simple random sample and some complex designs. On the other hand, Hartley and Silken (1975), Fuller (1975), Francisco and Fuller (1991) and others, studied the properties of the sample estimator with respect to a model parameter, some times called superpopulation parameter. They obtained asymptotic results for regression sample estimators using data from certain complex sampling designs. Underlying their set ups, there was the notion of a "superpopulation" defined on a probability space (~,F,P) and the finite population was a considered a realization of it for an outcome to e [Z . The observed sample would be the second phase...

Applications of quasi-Monte Carlo methods in inference for complex survey data

Abstract This paper proposes a new method for estimating variances of complex survey estimators based on the recent developments in quasi-Monte Carlo methods. The method can be effectively used to create replication schemes in complex surveys with designs more complex than 2 PSU/stratum, while other methods such as the survey bootstrap carry with them a substantial computational burden, as well as somewhat larger instability.

Resampling variance estimation for complex survey data

2010

Abstract. In this article, I discuss the main approaches to resampling variance estimation in complex survey data: balanced repeated replication, the jackknife, and the bootstrap. Balanced repeated replication and the jackknife are implemented in the Stata svy suite. The bootstrap for complex survey data is implemented by the bsweights command. I describe this command and provide working examples.

Applications of quasi-Monte Carlo methods in survey inference

This work aims at proposing a new method for estimating variances of complex survey estimators based on the recent developments in quasi-Monte Carlo methods. It can be effectively used to create replication schemes in complex surveys where the mathematically elegant schemes such as balanced repeated replications break down due to design complexities, while other methods such as the survey bootstrap carry with them a substantial computational burden, as well as somewhat larger instability.

Resampling Inference with Complex Survey Data

Two PSUs per strata (nh= 2)—can use BRR: delete one of two units in each stratum, repeat S times. Can do this efficiently by borrowing from the factorial experiment design literature: a minimal number of BRR resamples (to estimate variance in each stratum) is L≤ S≤ L+ 3. y (j)