Calibrated Weighting for Small Area Estimation

Calibrated 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

Estimation of Production Functions on Fishery: A Danish Survey

The fishing fleet and the component parts of effort and production can be de-scribed and analysed in different ways. As an example, the fishing fleet can be described using a list of different production function specifications. These pro-duction functions will in this paper be estimated using data for the Danish North Sea human consumption demersal trawl fishery. Some statistical prob-lems including multicollinearity are discussed and possible solutions and inter-pretations are put forward.Danish North Sea human consumption demersal trawl fishery, pro-duction function, multicollinearity

Estimating Photometric Redshifts of Quasars via K-nearest Neighbor Approach Based on Large Survey Databases

We apply one of lazy learning methods named k-nearest neighbor algorithm (kNN) to estimate the photometric redshifts of quasars, based on various datasets from the Sloan Digital Sky Survey (SDSS), UKIRT Infrared Deep Sky Survey (UKIDSS) and Wide-field Infrared Survey Explorer (WISE) (the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN arrives at the best performance when k is different with a special input pattern for a special dataset. The best result belongs to the SDSS-UKIDSS-WISE sample. The experimental results show that generally the more information from more bands, the better performance of photometric redshift estimation with kNN. The results also demonstrate that kNN using multiband data can effectively solve the catastrophic failure of photometric redshift estimation, which is met by many machine learning methods. By comparing the performance of various methods for photometric redshift estimation of quasars, kNN based on KD-Tree shows its superiority with the best accuracy for our case.Comment: 28 pages, 4 figures, 3 tables, accepted for publication in A

Aspects of Estimation Procedures at Eurostat with Some Emphasis on Over-Space Harmonisation

It is of high interest for Eurostat, the investigation of the different estimation procedures that are applied, or discussed, internally. We focus our interest on three estimation domains i.e. the micro-aggregation techniques for producing confidential data, the backward calculation methods for obtaining homogeneous time series and some aspects of the sampling procedures that are discussed by Eurostat and are applied in the Member State level. With regard to each domain of estimation, we describe the different estimation procedures that are applied and the criteria for assessing the quality of the results obtained, and we make some proposals for the adoption of better practices. Due to the multinational character of the third estimation domain and in order to achieve the targets of our description, we used as exploratory tools three sample surveys that are conducted in all Member State i.e. the Labour Force survey, the European Household Panel survey and the Household Budget survey. Especially for those estimation domains that are applied at National level, we examined attempts that aim at the over space harmonization of the estimation procedures or of the measured concepts, and the role that Eurostat adopts in relation to those harmonization attempts

Pseudo Bayesian Estimation of One-way ANOVA Model in Complex Surveys

We devise survey-weighted pseudo posterior distribution estimators under 2-stage informative sampling of both primary clusters and secondary nested units for a one-way ANOVA population generating model as a simple canonical case where population model random effects are defined to be coincident with the primary clusters. We consider estimation on an observed informative sample under both an augmented pseudo likelihood that co-samples random effects, as well as an integrated likelihood that marginalizes out the random effects from the survey-weighted augmented pseudo likelihood. This paper includes a theoretical exposition that enumerates easily verified conditions for which estimation under the augmented pseudo posterior is guaranteed to be consistent at the true generating parameters. We reveal in simulation that both approaches produce asymptotically unbiased estimation of the generating hyperparameters for the random effects when a key condition on the sum of within cluster weighted residuals is met. We present a comparison with frequentist EM and a methods that requires pairwise sampling weights.Comment: 46 pages, 9 figure

Standard survey methods for estimating colony losses and explanatory risk factors in Apis mellifera

This chapter addresses survey methodology and questionnaire design for the collection of data pertaining to estimation of honey bee colony loss rates and identification of risk factors for colony loss. Sources of error in surveys are described. Advantages and disadvantages of different random and non-random sampling strategies and different modes of data collection are presented to enable the researcher to make an informed choice. We discuss survey and questionnaire methodology in some detail, for the purpose of raising awareness of issues to be considered during the survey design stage in order to minimise error and bias in the results. Aspects of survey design are illustrated using surveys in Scotland. Part of a standardized questionnaire is given as a further example, developed by the COLOSS working group for Monitoring and Diagnosis. Approaches to data analysis are described, focussing on estimation of loss rates. Dutch monitoring data from 2012 were used for an example of a statistical analysis with the public domain R software. We demonstrate the estimation of the overall proportion of losses and corresponding confidence interval using a quasi-binomial model to account for extra-binomial variation. We also illustrate generalized linear model fitting when incorporating a single risk factor, and derivation of relevant confidence intervals