Search

In the last decades policy decisions are often based on statistical measures. The more detailed this information is, the better is the basis for targeting policies and evaluating policy programs. For instance, the United Nations suggest more disaggregation of statistical indicators for monitoring their Sustainable Development Goals and also the number of National Statistical Institutes (NSIs) that notice the need of more disaggregated statistics is increasing. Dimensions for disaggregation can be characteristics of the individuals or households like sex, age or ethnicity, economic activity or spatial dimensions like metropolitan areas or districts. Primary data sources for variables that are used to estimate statistical indicators are national household surveys. However, sample sizes are usually small or even zero at disaggregated levels. Therefore, direct estimators based only on survey data can be unreliable or not available for small domains. While the option of more specific surveys is costly, model-based methodologies for dealing with small sample sizes can help to obtain reliable estimates for small domains. The so-called Small Area Estimation (SAE) methods [1,2] link survey data that is only available for a proportion of households with administrative or census data available for all households in the area of interest. Even though a wide range of SAE methods is proposed by academic researchers, these are, so far, applied only by a small number of NSIs or other practitioners like the World Bank. This gap between theoretical possibilities and practical application can have several reasons. One reason can be the lack of suitable statistical software. The free software environment R helps to counteract this issue since researchers can make their codes available to the public via packages. Thus, new methods can reach the practitioner faster than with non-free software. The next two sections summarize which packages are already available and what could be improved in the future.

Consumer Price Indices (CPI) are used in many ways by the government, businesses, and society in general. They can affect interest rates, tax allowances, wages, state benefits, and many other payments. The CPI is a fixed (national) basket index, where a range of goods and services is priced each month, and the expenditure shares on items in the basket are used to weight the price information together. The starting point for a regional price index should be a regional basket of goods and services. In the current poster, we derive regional baskets from the UK Living Costs and Food Survey (LCF), taking the products (COICOP classification) with the largest proportion of expenditures. As the sample size is naturally much smaller for regions, the accuracy of the direct estimates on the basket will be reduced. In order to overcome this problem one possibility - discussed in the poster - is to pool multiple years of LCF data to increase the sample size. Another is to consider small area estimation approaches for the regional basket. Ideally, the small area estimates would be constrained to the overall expenditure total. Therefore, we assess some benchmarking approaches. Since the conceptual framework of CPI-calculationfor the UK and Germany do not differ too much the presented methodology can also be adapted for the calculation of regional CPIs for Germany.

Among a variety of small area estimation methods, one popular approach for the estimation of linear and non-linear indicators is the empirical best predictor. However, parameter estimation using standard maximum likelihood methods is not possible, when the dependent variable of the underlying nested error regression model, is censored to specific intervals. This is often the case for income variables. Therefore, this work proposes an estimation method, which enables the estimation of the regression parameters of the nested error regression model using interval censored data. The introduced method is based on the stochastic expectation maximization algorithm. Since the stochastic expectation maximization method relies on the Gaussian assumptions of the error terms, transformations are incorporated into the algorithm to handle departures from normality. The estimation of the mean squared error of the empirical best predictors is facilitated by a parametric bootstrap which captures the additional uncertainty coming from the interval censored dependent variable. The validity of the proposed method is validated by extensive model-based simulations.

Geographically weighted small area methods have been studied in literature for small area estimation. Although these approaches are useful for the estimation of small area means efficiently under strict parametric assumptions, they can be very sensitive to outliers in the data. In this paper, we propose a robust extension of the geographically weighted empirical best linear unbiased predictor (GWEBLUP). In particular, we introduce robust projective and predictive small area estimators under spatial non-stationarity. Mean squared error estimation is performed by two different analytic approaches that account for the spatial structure in the data. The results from the model- based simulations indicate that the proposed approach may lead to gains in terms of efficiency. Finally, the methodology is demonstrated in an illustrative application for estimating the average total cash costs for farms in Australia.

Modern systems of official statistics require the accurate and timely estimation of socio-demographic indicators for disaggregated geographical regions. Traditional data collection methods such as censuses or household surveys impose great financial and organizational burdens for National Statistical Institutes. The rise of new information and communication technologies offers promising sources to mitigate these shortcomings. In this paper we propose a unified approach for National Statistical Institutes based on small area estimation that allows for the estimation of socio-demographic indicators by using mobile phone data. In particular, the methodology is applied to mobile phone data from Senegal for deriving sub-national estimates of the share of illiterates disaggregated by gender. The estimates are used to identify hot spots of illiterates with a need for additional infrastructure or policy adjustments. Although the paper focuses on literacy as a particular socio-demographic indicator, the proposed approach is applicable to indicators from national statistics in general.

In this paper we will present recent work on a new unit-level small area methodology that can be used with continuous and discrete outcomes. The proposed method is based on constructing a model-based estimator of the distribution function by using a nested-error regression model for the quantiles of the target outcome. A general set of domain-specific parameters that extends beyond averages is then estimated by sampling from the estimated distribution function. For fitting the model we exploit the link between the Asymmetric Laplace Distribution and maximum likelihood estimation for quantile regression. The specification of the distribution of the random effects is considered in some detail by exploring the use of parametric and non- parametric alternatives. The use of the proposed methodology with discrete (count) outcomes requires appropriate transformations, in particular jittering. For the case of discrete outcomes the methodology relaxes the restrictive assumptions of the Poisson generalised linear mixed model and allows for what is potentially a more flexible mean-variance relationship. Mean Squared Error estimation is discussed. Extensive model-based simulations are used for comparing the proposed methodology to alternative unit-level methodologies for estimating a broad range of complex parameters.

Modern systems of official statistics require the estimation and publication of business statistics for disaggregated domains, for example, industry domains and geographical regions. Outlier robust methods have proven to be useful for small-area estimation. Recently proposed outlier robust model-based small-area methods assume, however, uncorrelated random effects. Spatial dependencies, resulting from similar industry domains or geographic regions, often occur. In this paper, we propose an outlier robust small-area methodology that allows for the presence of spatial correlation in the data. In particular, we present a robust predictive methodology that incorporates the potential spatial impact from other areas (domains) on the small area (domain) of interest. We further propose two parametric bootstrap methods for estimating the mean-squared error. Simulations indicate that the proposed methodology may lead to efficiency gains. The paper concludes with an illustrative application by using business data for estimating average labour costs in Italian provinces.

Arthroscopic treatment of hallux rigidus is appropriate after failed nonoperative treatment. Debridement with cheilectomy, and fusion are the main indications for arthroscopic treatment of hallux rigidus. If the cartilage damage is extensive and the patient has consented, then a fusion is performed at the same sitting.

Health-related quality of life assessment is important in the clinical evaluation of patients with metastatic disease that may offer useful information in understanding the clinical effectiveness of a treatment. To assess if a set of explicative variables impacts on the health-related quality of life, regression models are routinely adopted. However, the interest of researchers may be focussed on modelling other parts (e.g. quantiles) of this conditional distribution. In this paper we present an approach based on M-quantile regression to achieve this goal. We applied the proposed methodology to a prospective, randomized, multi-centre clinical trial. In order to take into account the hierarchical nature of the data we extended the M-quantile regression model to a three-level random effects specification and estimated it by maximum likelihood.

Objective: Fixed cavovarus deformity can lead to anteromedial ankle arthrosis due to elevated medial joint contact stresses. Lateralizing calcaneal osteotomies and tibial osteotomies are commonly used to treat symptoms and redistribute joint contact forces. In a former study a significant lateral shift[for full text, please go to the a.m. URL], Deutscher Kongress für Orthopädie und Unfallchirurgie (DKOU 2013)

The demand for reliable statistics has been growing over the past decades, because more and more political and economic decisions are based on statistics, e.g. regional planning, allocation of funds or business decisions. Therefore, it has become increasingly important to develop and to obtain precise regional indicators as well as disaggregated values in order to compare regions or specific groups. In general, surveys provide the information for these indicators only for larger areas like countries or administrative divisions. However, in practice, it is more interesting to obtain indicators for specific subdivisions like on NUTS 2 or NUTS 3 levels. The Nomenclature of Units for Territorial Statistics (NUTS) is a hierarchical system of the European Union used in statistics to refer to subdivisions of countries. In many cases, the sample information on such detailed levels is not available. Thus, there are projects such as the European Census, which have the goal to provide precise numbers on NUTS 3 or even community level. The European Census is conducted amongst others in Germany and Switzerland in 2011. Most of the participating countries use sample and register information in a combined form for the estimation process. The classical estimation methods of small areas or subgroups, such as the Horvitz-Thompson (HT) estimator or the generalized regression (GREG) estimator, suffer from small area-specific sample sizes which cause high variances of the estimates. The application of small area methods, for instance the empirical best linear unbiased predictor (EBLUP), reduces the variance of the estimates by including auxiliary information to increase the effective sample size. These estimation methods lead to higher accuracy of the variables of interest. Small area estimation is also used in the context of business data. For example during the estimation of the revenues of specific subgroups like on NACE 3 or NACE 4 levels, small sample sizes can occur. The Nomenclature statistique des activités économiques dans la Communauté européenne (NACE) is a system of the European Union which defines an industry standard classification. Besides small sample sizes, business data have further special characteristics. The main challenge is that business data have skewed distributions with a few large companies and many small businesses. For instance, in the automotive industry in Germany, there are many small suppliers but only few large original equipment manufacturers (OEM). Altogether, highly influential units and outliers can be observed in business statistics. These extreme values in connection with small sample sizes cause severe problems when standard small area models are applied. These models are generally based on the normality assumption, which does not hold in the case of outliers. One way to solve these peculiarities is to apply outlier robust small area methods. The availability of adequate covariates is important for the accuracy of the above described small area methods. However, in business data, the auxiliary variables are hardly available on population level. One of several reasons for that is the fact that in Germany a lot of enterprises are not reflected in business registers due to truncation limits. Furthermore, only listed enterprises or companies which trespass specific thresholds are obligated to publish their results. This limits the number of potential auxiliary variables for the estimation. Even though there are issues with available covariates, business data often include spatial dependencies which can be used to enhance small area methods. Next to spatial information based on geographic characteristics, group-specific similarities like related industries based on NACE codes can be used. For instance, enterprises from the same NACE 2 level, e.g. sector 47 retail trade, behave more similar than two companies from different NACE 2 levels, e.g. sector 05 mining of coal and sector 64 financial services. This spatial correlation can be incorporated by extending the general linear mixed model trough the integration of spatially correlated random effects. In business data, outliers as well as geographic or content-wise spatial dependencies between areas or domains are closely linked. The coincidence of these two factors and the resulting consequences have not been fully covered in the relevant literature. The only approach that combines robust small area methods with spatial dependencies is the M-quantile geographically weighted regression model. In the context of EBLUP-based small area models, the combination of robust and spatial methods has not been considered yet. Therefore, this thesis provides a theoretical approach to this scientific and practical problem and shows its relevance in an empirical study., In den letzten Jahren gab es einen steigenden Bedarf an disaggregierten Wirtschaftsdaten, da immer mehr Unternehmen und Institutionen ihre Entscheidungen auf Basis solcher Informationen treffen. Diese tiefgegliederten Daten können beispielsweise Umsätze von Unternehmen in einer speziellen Industrieklasse (NACE3- oder NACE4-Ebene) sein. Allerdings sind in der Realität Stichproben auf solch detailliertem Niveau selten verfügbar, was in der Regel kleine Stichprobenumfänge bewirkt. In solchen Fällen führen klassische Schätzverfahren oft zu schlechten Ergebnissen und Small Area Methoden müssen angewendet werden. Das Grundprinzip von diesen Verfahren besteht darin, dass die Schätzung durch Erweiterung der ursprünglich zu kleinen Datengruppe verbessert wird. Dies kann durch die Kombination der Erhebungsinformationen mit Hilfsvariablen, wie zum Beispiel Unternehmensdaten aus ähnlich strukturierten Industriezweigen, erreicht werden. Diese Methoden liefern unter strikten Annahmen über die Stichprobe und die Verteilung selbst bei kleinen Stichprobenumfängen deutlich bessere Schätzergebnisse als klassische Verfahren. Des Weiteren beinhalten einzelne Industrieklassen stark unterschiedlich ausgeprägte Werte. Ein Beispiel hierfür ist die Automobilindustrie, in der neben vielen kleineren Zulieferunternehmen auch einige große Hersteller in dieser Branche zu finden sind. Solche stark abweichenden Beobachtungen werden in der Statistik als Ausreißer bezeichnet. Bei Small Area Verfahren führt die Kombination von Ausreißern mit kleinen Stichprobenumfängen zu starken Problemen. Aus diesem Grund müssen Small Area Methoden gegenüber solch starken Beobachtungen robustifiziert werden. Derzeit gibt es in diesem Bereich zwei Ansätze: den robusten EBLUP und M-Quantile Modelle. Neben Ausreißern weisen aber Unternehmensdaten oft auch räumliche Muster auf, wie zum Beispiel geografische Nachbarschaftsstrukturen oder Branchenähnlichkeiten. Diese räumlichen Strukturen können ebenfalls benutzt werden, um die ursprünglich zu kleine Datengruppe zu erweitern. Die gleichzeitige Berücksichtigung von Ausreißern und räumlichen Mustern bei Schätzverfahren ist in der Wissenschaft jedoch noch nicht vollständig untersucht. Aus diesem Grund widmet sich die Arbeit dieser wissenschaftlich relevanten Problemstellung.