5,647 research outputs found
Multipurpose small area estimation
No full textSample surveys are generally multivariate, in the sense that they measure more than oneresponse variable. In theory, each variable can then be assigned an optimal weight forestimation purposes. However, it is often a distinct practical advantage to have a singleweight that is used with all variables collected in the survey. This paper describes howsuch multipurpose sample weights can be constructed when small area estimates of thesurvey variables are required. The approach is based on the model-based direct (MBD)method of small area estimation described in Chambers and Chandra (2006). Empiricalresults reported in this paper show that MBD estimators for small areas based onmultipurpose weights perform well across a range of variables that are often of interest inbusiness surveys. Furthermore, these results show that the proposed approach is robust tomodel misspecification and also efficient for the variables ill-suited to standard methodsof small area estimation (e.g. variables that contain a significant proportion of zeros).<br/
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
Improved Direct Estimators for Small Areas
No full textUnbiased direct estimators for small area quantities are usually considered too variable to be of any practical use. In this paper we propose a class of model-based direct estimators for small area quantities that appears to overcome this objection, in the sense that these estimators are comparable in efficiency to the indirect model-based small area estimators (e.g. empirical best linear unbiased predictors, or EBLUPs) that are now widely used. There are many practical advantages associated with such model-based direct (MBD) estimators, arising from the fact that they are computed as weighted linear combinations of the actual sample data from the small areas of interest. Note that in this case the weights ‘borrow strength’ via a model that explicitly allows for small area effects. One particular advantage that we explore in this paper is that estimation of mean squared error (MSE) is then straightforward, using well-known methods that are in common use for population level estimates. Empirical results reported in this paper show that the MBD estimator represents a real alternative to the EBLUP, with the simple MSE estimator associated with the MBD estimator providing good coverage performance. We also report results that indicate that the MBD estimator may be more robust than the EBLUP when the small area model is incorrectly specified. Furthermore, the MBD approach is easily extended to provide multi-purpose weights that are efficient across a range of variables, including variables that are unsuitable for EBLUP, e.g. variables that contain a significant proportion of zeros
Small Area Estimation with Skewed Data
No full textIn business surveys, data typically are skewed and the standard approach for small area estimation based on linear mixed models lead to inefficient estimates. In this paper, we discuss small area estimation techniques for skewed data that are linear following a suitable transformation. In this context, implementation of the empirical best linear unbiased prediction (EBLUP) approach under transformation to a linear mixed model is complicated. However, this is not the case with the model-based direct (MBD) approach (Chambers and Chandra, 2006), which is based on weighted linear estimators. We extend the MBD approach to skewed data using sample weights derived via model calibration based on a log transform model with random area effects. Our results show this estimator is both efficient and robust with respect to the distribution of these random effects. An application to real data demonstrates the satisfactory performance of the method
Portrait of Vassilie Trunoff, in the J.C. Williamson production of Oklahoma! [picture] /
No full textInscriptions:"Vassilie Trunoff" -- typed in red lower right; "Hal Williamson, photographic illustrator, no. 2833, Reiby Chambers, Reiby Place, Sydney, N.S.W., phone BU 3172"; "J.C.W. Publicity, Sydney" -- stamps on reverse; "P5, 1 col x 2 3/8 " in box" -- in pencil on reverse.; Condition: Good, pin-holes in corners.; Part of the collection: J.C. Williamson collection of photographs.; Also available in an electronic version via the Internet at: http://nla.gov.au/nla.pic-vn3064651
Assessment of fissionable material behaviour in fission chambers
No full textA comprehensive study is performed in order to assess the pertinence of fission chambers coated with different fissile materials for high neutron flux detection. Three neutron scenarios are proposed to study the fast component of a high neutron flux: (i) high neutron flux with a significant thermal contribution such as BR2, (ii) DEMO magnetic fusion reactor, and (iii) IFMIF high flux test module.
In this study, the inventory code ACAB is used to analyze the following questions: (i) impact of different deposits in fission chambers; (ii) effect of the irradiation time/burn-up on the concentration; (iii) impact of activation cross-section uncertainties on the composition of the deposit for all the range of burn-up/irradiation neutron fluences of interest. The complete set of nuclear data (decay, fission yield, activation cross-sections, and uncertainties) provided in the EAF2007 data library are used for this evaluation
M-Quantile Models for Small Area Estimation
No full textSmall area estimation techniques are employed when sample data are insufficient for acceptably precise direct estimation in domains of interest. These techniques typically rely on regression models that use both covariates and random effects to explain variation between domains. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. We describe a new approach to small area estimation that is based on modelling quantile-like parameters of the conditional distribution of the target variable given the covariates. This avoids the problems associated with specification of random effects, allowing inter-domain differences to be characterized by the variation of area-specific M-quantile coefficients. The proposed approach is easily made robust against outlying data values and can be adapted for estimation of a wide range of area specific parameters, including that of the quantiles of the distribution of the target variable in the different small areas. Results from two simulation studies comparing the performance of the M-quantile modelling approach with more traditional mixed model approaches are also provided
Maximum Likelihood with Auxiliary Information
No full textAnalysis of survey data does not happen in a vacuum. We typically know more about the target population than just the data observed in the survey. In some cases this extra information can be incorporated via calibration of survey weights. However, model fitting using weights often leads to increased standard errors. Also, weights are usually calibrated to a relatively small set of variables, while population data may be known for many more variables. Here we use the general approach to maximum likelihood estimation for complex surveys described in Breckling et al. (1994) to develop methods for efficiently incorporating external population information into model fitting using survey data. In particular, we focus on two simple, but very popular, models fitted to survey data. These are the linear regression model and the logistic regression model
Characterization of molecular and atomic oxygen ZnO thick film sensor by impedance spectroscopy
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