1,722,214 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
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
Small area estimation via m-quantile geographically weighted regression
No full textThe effective use of spatial information, that is the geographic locations of population units, in a regression model-based approach to small area estimation is an important practical issue. One approach for incorporating such spatial information in a small area regression model is via Geographically Weighted Regression (GWR). In GWR the relationship between the outcome variable and the covariates is characterised by local rather than global parameters, where local is defined spatially. In this paper we investigate GWR-based small area estimation under the M-quantile modelling approach. In particular, we specify an M-quantile GWR model that is a local model for the M-quantiles of the conditional distribution of the outcome variable given the covariates. This model is then used to define a bias-robust predictor of the small area characteristic of interest that also accounts for spatial association in the data. An important spin-off from applying the M-quantile GWR small area model is that it can potentially offer more efficient synthetic estimation for out of sample areas. We demonstrate the usefulness of this framework through both model-based as well as design-based simulations, with the latter based on a realistic survey data set. The paper concludes with an illustrative application that focuses on estimation of average levels of Acid Neutralizing Capacity for lakes in the north-east of the USA.<br/
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
Outlier Robust Imputation of Survey Data via Reverse Calibration
No full textOutlier robust methods of survey estimation, e.g. trimming, winsorization, are well known (Chambers and Kokic, 1993). However, such methods do not address the important practical problem of creating an “outlier free” data set for general and public use. In particular, what is required in this situation is a data set from which the outlier robust survey estimate can be recovered by the application of standard methods of survey estimation. In this paper we describe an imputation procedure for outlying survey values, called reverse calibration, that achieves this aim. This method can also be used to correct gross errors in survey data, as well as to impute missing values. The paper concludes with an evaluation of the method based on a realistic survey data set
Small Area Methodology in Poverty Mapping: An introductory overview
No full textPoverty and inequality remain at the top of the global economic agenda, and the methodology of measuring poverty continues to be a key area of research. This chapter offers an up to date survey of new methods for estimating poverty at local leve
- …
