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*Sahar Z. Roderick J.*

- Design and Inference in Finite Population Sampling
- Design and Inference in Finite Population Sampling
- Design and Inference in Finite Population Sampling
- Design and Inference in Finite Population Sampling

JavaScript is disabled for your browser. Some features of this site may not work without it. Inference from finite population sampling : a unified approach. Author Hargovan, Kashmira Ansuyah. Metadata Show full item record.

## Design and Inference in Finite Population Sampling

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The problem of handling non-ignorable non-response has been typically addressed under the design-based approach using the well-known sub-sampling technique introduced by Hansen and Hurwitz [, Journal of the American Statistical Association, Vol 41 , Page ]. Alternatively, the model-based paradigm emphasizes on utilizing the underlying model relationship between the outcome variable and one or more covariate s whose population values are known prior to the survey. This article utilizes the model relationship between the study variable and covariate s for handling non-ignorable non-response and obtaining an unbiased estimator for the population total under the sub-sampling technique. The main idea is to combine the estimates obtained from the sample on first call and the sub-sample from second call using separate model relationships. The contribution of this paper helps us in providing unbiased estimates with an improved efficiency under model-based paradigm in presence of non-ignorable non-response.

## Design and Inference in Finite Population Sampling

The role of the sample selection mechanism in a model-based approach to finite population inference is examined. When the data analyst has only partial information on the sample design then a design which is ignorable when known fully may become informative. Conditions under which partially known designs can be ignored are established and examined for some standard designs. The results are illustrated by an example used by Scott Most users should sign in with their email address. If you originally registered with a username please use that to sign in.

My research in survey sampling focuses on model-based Bayesian methods for complex survey designs that are robust to misspecification, and comparing the resulting inferences to classical methods based on the randomization distribution. Methods for survey nonresponse are discussed in the section on missing data research. Little, R. Journal of Official Statistics , 28, 3, Statistical Science 26, 2, DOI: Chen, Q.

There are two general views in causal analysis of experimental data: the super population view that the units are an independent sample from some hypothetical infinite population, and the finite population view that the potential outcomes of the experimental units are fixed and the randomness comes solely from the treatment assignment. These two views differs conceptually and mathematically, resulting in different sampling variances of the usual difference-in-means estimator of the average causal effect. Practically, however, these two views result in identical variance estimators. By recalling a variance decomposition and exploiting a completeness-type argument, we establish a connection between these two views in completely randomized experiments. This alternative formulation could serve as a template for bridging finite and super population causal inference in other scenarios. Neyman [ 1 , 2 ] defined causal effects in terms of potential outcomes, and proposed an inferential framework viewing all potential outcomes of a finite population as fixed and the treatment assignment as the only source of randomness.

## Design and Inference in Finite Population Sampling

Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. The final technical session of the workshop covered analysis techniques for small population and small sample research. Rick H. Hoyle Duke University described design and analysis considerations in research with small populations.

### Design and Inference in Finite Population Sampling

This is a preview of subscription content, access via your institution. Rent this article via DeepDyve. Reprints and Permissions. Rao, P. Design and inference in finite population sampling. Metrika 41, 42 Download citation.

Most statistical theory is premised on an underlying infinite population. By contrast, survey sampling theory and practice are built on a foundation of sampling from a finite population. This basic difference has myriad ramifications, and it highlights why survey sampling is often regarded as a separate branch of statistical thinking. On a philosophical level, the theory brings statistical theory to a human, and thus necessarily finite, level.

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Finite Population Sampling * Comparisons with Design-Based Regression Estimation, Exercises Robustness and Design-Based Inference,

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Мозг Хейла лихорадочно работал.

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* Когда мы внесем эту поправку, - добавил Стратмор, - мне будет все равно, сколько ключей гуляет по свету: чем их больше, тем забавнее.*

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detailed. Another interesting methodology discussed is related to file-merging problems arising in official statistics. The chapter concludes with a detailed.