1,720,984 research outputs found
Calibrated Weighting for Small Area Estimation
No full textCalibrated weighting methods for estimation of survey population characteristics are widely used. At the same time, model-based prediction methods for estimation of small area or domain characteristics are becoming increasingly popular. This paper explores weighting methods based on the mixed models that underpin small area estimates to see whether they can deliver equivalent small area estimation performance when compared with standard prediction methods and superior population level estimation performance when compared with standard calibrated weighting methods. A simple MSE estimator for weighted small area estimation is also developed
Which Sample Survey Strategy? A Review of Three Different Approaches
No full textSample survey theory is concerned with methods of sampling from a finite populationof N units and then making inferences about finite population quantities on the basis of the sample data. A method of sampling coupled with a method of estimation given the sample data is often referred to as a sampling strategy, and typically corresponds to a set of rules which tell one how to obtain a sample of units from the finite population and then how to manipulate the resulting sample data to estimate the value of a quantity defined for the entire population
Imputation vs. Estimation of Finite Population Distributions
No full textEstimates of the distribution of hourly wage rates for employees are an important output for a national statistics agency. However, many employees are not paid by the hour and so their hourly wage rate data are effectively missing in a survey that attempts to collect this information. A standard approach in this situation is to impute these missing values using derived measures of this wage rate based on salary and hours worked data also collected in the survey. This paper contrasts this imputation approach with direct estimation of the wage rate distribution using the derived wage rate variable as an auxiliary. In particular, we focus on data obtained in the 2002 UK New Earnings Survey and use simulation based on actual and derived hourly wage rate data collected in this survey to compare two imputation approaches, one based on substituting the derived wage rate values for the missing actual values, the other using nearest neighbour imputation based on the derived wage rate, with two estimation approaches that use this variable as an auxiliary. The first of these is a semi-parametric extension of the Chambers and Dunstan (1986) estimator of the finite population distribution function, the other is a calibrated spline-based estimator of this function recently suggested by Harms and Duchesne (2004). Our conclusion is that an approach based on the semi-parametric estimator is best for these data. However, confidence interval estimation remains an open problem
What If... ? Robust Prediction Intervals for Unbalanced Samples
No full textA confidence interval is a standard way of expressing our uncertainty about the value of a population parameter. In survey sampling most methods of confidence interval estimation rely on “reasonable” assumptions to be true in order to achieve nominal coverage levels. Typically these correspond to replacing complex sample statistics by large sample approximations and invoking central limit behaviour. Unfortunately, coverage of these intervals in practice is often much less than anticipated, particularly in unbalanced samples. This paper explores an alternative approach, based on a generalisation of quantile regression analysis, to defining an interval estimate that captures our uncertainty about an unknown population quantity. These quantile-based intervals seem more robust and stable than confidence intervals, particularly in unbalanced situations. Furthermore, they do not involve estimation of second order quantities like variances, which is often difficult and time-consuming for non-linear estimators. We present empirical results illustrating this alternative approach and discuss implications for its use
Robust Sample Survey Inference via Bootstrapping and Bias Correction: The Case of the Ratio Estimator
No full textThe bootstrap approach to statistical inference is described in Efron (1982). The method has wide applicability and has seen considerable development in recent years. However, use of the bootstrap in sample survey inference has been somewhat limited. Rao and Wu (1988), describe an application of the bootstrap under the design-based approach to sample survey inference. Sitter (1992a, 1992b), has extended their results to more complex survey designs. More recently, Booth, Butler and Hall (1991) and Booth and Murison (1992) describe a rather different approach to constructing a design-based bootstrap. In this paper we describe how this approach to the bootstrap can be applied under model-based sample survey inference, focussing on an application where the popular ratio estimator is the estimator of choice
Small area estimation with linked data
Full text linkData linkage can be used to combine values of the variable of interest from a national survey with values of auxiliary variables obtained from another source, such as a popula- tion register, for use in small area estimation. However, linkage errors can induce bias when fitting regression mod- els; moreover, they can create non-representative outliers in the linked data in addition to the presence of potential representative outliers. In this paper, we adopt a second- ary analyst’s point of view, assuming that limited information is available on the linkage process, and develop small area estimators based on linear mixed models and M-quantile models to accommodate linked data containing a mix of both types of outliers. We illustrate the properties of these small area estimators, as well as estimators of their mean squared error, by means of model-based and design- based simulation experiments. We further illustrate the proposed methodology by applying it to linked data from the European Survey on Income and Living Conditions and the Italian integrated archive of economic and demo- graphic micro data in order to obtain estimates of the aver- age equivalised income for labour market areas in central Italy
Small area estimation with linked data
No full textData linkage can be used to combine values of the variable of interest from a national survey with values of auxiliary variables obtained from another source, such as a population register, for use in small area estimation. However, linkage errors can induce bias when fitting regression models; moreover, they can create non-representative outliers in the linked data in addition to the presence of potential representative outliers. In this paper, we adopt a secondary analyst’s point of view, assuming that limited information is available on the linkage process, and develop small area estimators based on linear mixed models and M-quantile models to accommodate linked data containing a mix of both types of outliers. We illustrate the properties of these small area estimators, as well as estimators of their mean squared error, by means of model-based and design-based simulation experiments. We further illustrate the proposed methodology by applying it to linked data from the European Survey on Income and Living Conditions and the Italian integrated archive of economic and demographic micro data in order to obtain estimates of the average equivalised income for labour market areas in central Italy
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
- …
