For many years survey sampling remained the province of survey samplers with very little input from statisticians involved in the more traditional aspects of the subject. Get a printable copy pdf file of the complete article 534k. Lucie leon, marie jauffretroustide, yann le strat, designbased inference in timelocation sampling, biostatistics, volume 16, issue 3. Sampling from finite populations encyclopedia of mathematics. In this work, we propose a new bootstrap method applicable to. Bootstrap is a useful tool for making statistical inference, but it may provide erroneous results in survey sampling if the sampling design is ignored. Full text full text is available as a scanned copy of the original print version. Design and inference in finite population sampling wiley series in survey methodology b. Alternative estimation method for a threestage cluster. Rubin indian institute of management calcutta department of statistics, harvard university abstract. Finite population sampling is perhaps the only area of statistics in which the primary mode of analysis is based on the randomization distribution, rather than on statistical models for the measured variables.
Hence, in addition to stratum structure, it induces an additional ranking structure within stratum samples. Modelbased inference re quires specification of a model that relates y u to a set of covariates predictors. Randomness comes from sampling alone unbiasedness over repeated sampling. Using standard tools from finite population sampling to improve. Raj, p4 all these four steps are interwoven and cannot be considered isolated from one another. Model for informative sampling and inference we have observed i. Bayesian finite population survey sampling sudipto banerjee division of biostatistics school of public health university of minnesota. This article considers causal inference for treatment contrasts from a randomized experiment using potential outcomes in a finite population setting. Pdf an introduction to sampling from a finite population. Sjps is constructed from a finite population using either a with or without replacement sampling design. Competing modes of inference for finite population sampling roderick j. Bootstrap inference for the finite population total under complex.
Inference for the population total from probability proportional to size pps sampling provides a comparison of design based and modelbased approac we use cookies to enhance your experience on our website. Design and inference in finite population sampling book. Iconstruct and implement a probability sampling design. Srss is constructed from a finite population using a without replacement sampling design.
L ittle 1 abstract we propose a bayesian penalized spline predictive bpsp estimator for a finite population proportion in an unequal probability sampling setting. Self and other in literary structure by rene girard 0pm. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. There are different terms that are used to describe the population, but the most commonly used is the target population, which is a finite set of elements to be studied. Get a printable copy pdf file of the complete article 534k, or click on a page image below. However, the term population of inference or inferential population is used more often during the conceptualization stage of research studies and surveys. This latter point is an important part of the material found in cochran 1977. Sorry, we are unable to provide the full text but you may find it at the following locations. We consider inference about the finite population total q y pop. We contrast inferences that are dependent on an assumed model with inferences based on the randomization induced by the sample selection plan. Design and inference in finite population sampling book, 1991. Bootstrap inference for the finite population total under. Causal inference in rebuilding and extending the recondite. Modelbased prediction theory for finite population sampling and inference valliant et al.
If valid estimates of the parameters of a finite population are to be produced, the finite population needs to be defined very precisely and the sampling method needs to be carefully designed and implemented. Little finite population sampling is perhaps the only area of statistics in which the primary mode of analysis is based on the randomization distribution, rather than on statistical models for the measured variables. Finite and infinite populations in biological statistics. The main feature of the proposed method is that the finite population is bootstrapped based on a multinomial distribution by incorporating the sampling weights. The corresponding numbers for the sample are n, m and k respectively. Design and inference in finite population sampling wiley. Statistical inference using stratified ranked set samples. In this work, we propose a new bootstrap method applicable to some complex sampling designs, including poisson sampling and probability proportional to size sampling. Design and inference in finite population sampling springerlink. Design and inference in finite population sampling wiley series in survey methodology sinha, b. Design and inference in finite population sampling ncbi nih. Real populations are finite and the branch of statistics which treats sampling of such populations is called survey sampling. In chaudhuri and stenger 1992, we see treatment of both designbased and modelbased sampling and inference.
Recall, a statistical inference aims at learning characteristics of the population from a sample. Designbased and modelbased inference in survey sampling. For most finitepopulation sampling schemes, estimators for simple population parameters, like mean or proportion, are different from those for. Alternative estimation method for a threestage cluster sampling in finite population.
Design and inference in finite population sampling journal. To make inference in the population from the random sample, a sampling weight is assigned to each surveyed individual. Designbased inference in timelocation sampling lucie leon. However, modelbased sampling can make use of randomization, and, further, the form of a designbased sample can be guided by the modeling of data. Simple random sampling, systematic sampling, stratified sampling fall into the category of simple sampling techniques. A prediction approach presents for the first time a unified treatment of sample design and estimation for finite. Modelbased inference is not predicated on probability sampling so it is a potentially attractive option for using vgi data that did not ori ginate from a probability sampling design. The sampling design does not play a critical role in modelbased inference although certain design structures such as clusters and strata may be represented in the model. The next and generally final step in computing adjusted design weights is to calibrate the nonresponse adjusted weights for responding units to sum to known. Most studies about bootstrapbased inference are developed under simple random sampling and stratified random sampling. Nov 01, 2009 the sampling frame is a list of primary sampling units in the finite population.
It is shown that if the finite population variables have a dirichletmultinomial prior, then the posterior distribution of the inobserved variables given a sample is also dirichletmultinomial. Estimation of population mean design based inference key idea. In this paper, we consider statistical inference based on poststratified samples from a finite population. Modelbased ideas in finite population sampling have received renewed discussion in recent years. With respect to research design and statistical analysis, a population is the entire collection of entities one seeks to understand or, more formally, about which one seeks to draw an inference consequently, defining clearly the population of interest is a fundamental component of research design because the way in which the population is defined dictates the scope of the inferences. Note that a finite population may be considered in several occasions. Evaluation and development of strategies for sample. Statistical inference using rankbased poststratified. Get a printable copy pdf file of the complete article 534k, or click on a page image below to browse page by page. A prediction approach presents for the first time a unified treatment of sample design and estimation for finite populations from a prediction point of view, providing readers with access to a wealth of theoretical results, including many new results and, a variety of practical applications. Bayesian statistical inference for sampling a finite.
In the designbased or randomization approach cochran 2007, y pop is treated as fixed, and inferences are based on the distribution of i i 1,i n. We look at the correspondence between the sampling design and the sampling scheme. New york chichester weinheim brisbane singapore toronto. Strategies based on probability proportional to size schemes of. Pdf file of the complete article 534k, or click on a page image below to browse page by page.
Finite population sampling and inference a prediction approach richard valliant alan h. Design and inference in finite population sampling core. Inference is constructed under both randomization theory and a super population model. Methodology for informative sampling we describe the bayesian model and inference in section 2. In brief, designbased inference is the classical approach to inference in survey sampling. Imputations may then be performed assuming iid data. Sampling distributions and statistical inference sampling distributions population the set of all elements of interest in a particular study. Finite population sampling and inference request pdf. Models in the practice of survey sampling revisited scb. Bayesian penalized spline modelbased inference for finite. Offers some important topics not found in other texts on sampling such as the superpopulation approach and randomized response, nonresponse and resampling techniques. Design and inference in finite population sampling wiley series in. Bayesian predictive inference for finite population. Causal inference in rebuilding and extending the recondite bridge between finite population sampling and experimental design rahul mukerjee, tirthankar dasgupta and donald b.
This approach automatically takes features of the survey design into. This entry focuses on the estimation of such finite population parameters using what is known as the randomization or designbased approach. An evaluation of modeldependent and probabilitysampling. Randomization consistency for finite population estimators is defined and adopted as a requirement of probability sampling. Covers a new but essential development in the field of population sampling, namely inference in finite sampling. However, modelbased sampling can make use of randomization, and, further, the form of a design based sample can be guided by the modeling of data.
The two inferential paradigms are constrasted, andexplanations. Inference is constructed under both randomized design and a super population model. Bayesian statistical inference for sampling a finite population is studied by using the dirichletmultinomial process as prior. Alternative modelbased and designbased frameworks for. By continuing to use our website, you are agreeing to our use of cookies. Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution. Uses of auxiliary size measures in survey sampling.
Bayesian inference for the finite population total from a. Strategiesbased on probability proportional to size schemes ofsampling. The sampling frame is a list of primary sampling units in the finite population. Sep 24, 2014 real populations are finite and the branch of statistics which treats sampling of such populations is called survey sampling. The first page of the pdf of this article appears above. Bootstrap inference for the finite population total under complex sampling designs. Littley abstract we study bayesian inference for the population total in probabilityproportionaltosize pps sampling.
In the case of finite population sampling, the statistician is free to choose his own sampling design and is not confined to independent and identically distributed observations as is often the case with traditional statistical inference. It is assumed that the observed data set is sampled from a larger population inferential statistics can be contrasted with descriptive. The two inferential paradigms are constrasted, andexplanations are supplemented. Bayesian penalized spline modelbased inference for finite population proportion in unequal probability sampling qixuanchen,michaelr. Design and inference in finite population sampling. Modelbased ideas in finitepopulation sampling have received renewed discussion in recent years. This entry focuses on the estimation of such finite population parameters using what is known as the randomization or design based approach. In chaudhuri and stenger 1992, we see treatment of both design based and modelbased sampling and inference. Bias, finite population, optimal estimation, prediction, random effects.
Statistical inference using stratified judgment post. We propose different variations of the weighted fpbb for different sampling designs, and evaluate these methods using three studies. Design and inference in finite population sampling wiley series in survey methodology. Their relationship to the classical ideas in sampling theorydo not appear to be universally well understood by samplers in applied disciplines such as forestry, and ecology more broadly. Designbased inference also known as randomization inference is concerned with infer ences about a finite population of size n, with fixed values for element i. Nonprobability sampling for finite population inference. Full text is available as a scanned copy of the original print version. We first select a simple random sample srs of size n and identify their population ranks. Finitepopulationsampling samplingofindependentobservations interestingfactsi i underindependentsamplingin. Bayesian predictive inference for finite population quantities under informative sampling. Statistics for social scientists quantitative social science research.