bluuebunny/crossref_metadata_2025_split huggingface dataset:
Triple-Goal Estimation of Unemployment Rates for U.S. States Using the U.S. Current Population Survey Data
Authors: Bonnéry Daniel , Cheng Yang , Ha Neung Soo , Lahiri Partha 
Published: November 2017

Abstract
In this paper, we first develop a triple-goal small area estimation methodology for simultaneous estimation of unemployment rates for U.S. states using the Current Population Survey (CPS) data and a two-level random sampling variance normal model. The main goal of this paper is to illustrate the utility of the triple-goal methodology in generating a single series of unemployment rate estimates for three separate purposes: developing estimates for individual small area means, producing empirical distribution function (EDF) of true small area means, and the ranking of the small areas by true small area means. We achieve our goal using a Monte Carlo simulation experiment and a real data analysis.
Covariate Selection for Small Area Estimation in Repeated Sample Surveys
Authors: van den Brakel Jan A. , Buelens Bart 
Published: November 2017

Abstract
If the implementation of small area estimation methods to multiple editions of a repeated sample survey is considered, then the question arises which covariates to use in the models. Applying standard model selection procedures independently to the different editions of the survey may identify different sets of covariates for each edition. If the small area predictions are sensitive to the different models, this is undesirable in official statistics since monitoring change over time of statistical quantities is of utmost importance. Therefore, potential confounding of true change and methodological alterations should be avoided. An approach to model selection is proposed resulting in a single set of covariates for multiple survey editions. This is achieved through conducting covariate selection simultaneously for all editions, minimizing the average of the edition-specific conditional Akaike Information Criteria. Consecutive editions of the Dutch crime victimization survey are used as a case study. Municipal estimates of three survey variables are obtained using area level models. The proposed averaging strategy is compared to the standard method of considering each edition separately, and to an elementary approach using covariates selected in the first edition. Resulting models, point estimates and MSE estimates are analyzed, indicating no substantial adverse effects of the conceptually attractive averaging strategy.
Small Area Estimation for Skewed Data in the Presence of Zeroes
Authors: Karlberg Forough 
Published: November 2017

Abstract
Skewed distributions with representative outliers pose a problem in many surveys. Various small area prediction approaches for skewed data based on transformation models have been proposed. However, in certain applications of those predictors, the fact that the survey data also contain a non-negligible number of zero-valued observations is sometimes dealt with rather crudely, for instance by arbitrarily adding a constant to each value (to allow zeroes to be considered as “positive observations, only smaller”, instead of acknowledging their qualitatively different nature).
On the other hand, while a lognormal-logistic model has been proposed (to incorporate skewed distributions as well as zeroes), that model does not include any hierarchical aspects, and is therefore not explicitly adapted to small area prediction.
In this paper, we consolidate the two approaches by extending one of the already established log-transformation mixed small area prediction models to incorporate a logistic component. This allows for the simultaneous, systematic treatment of domain effects, outliers and zero-valued observations in a single framework. We benchmark the resulting model-based predictors (against relevant alternatives) in applications to simulated data as well as empirical data from the Australian Agricultural and Grazing Industries Survey.
Sae Education Challenges to Academics and NSI1
Authors: Gołata Elżbieta 
Published: November 2017

Abstract
The aim of the paper is to present some experiences in teaching Small Area Estimation (SAE). SAE education experiences and challenges are analysed from the academic side and from the NSI side. An attempt was undertaken to discuss SAE issues in a wider perspective of teaching statistics. In particular, the topics refer to Polish conditions, but they are presented against the background of selected international experiences and practices. Information comes from a special inquiry - a survey conducted among employees of statistical offices and academics from universities involved in SAE research. A further issue is inclusion of SAE in the EMOS project (European Master in Official Statistics). The survey is extended with information collected by monitoring of trainings and projects organized by the leading centres dealing with SAE. The results obtained are related to a similar survey within Eurostat project: ESSnet on Small Area Estimation, which was conducted in 2010. The study includes interest in learning and the need to implement SAE methodology, a range of subjects taught as well as a range of applications, forms of training, type of courses, software used and teaching methods. In particular, it intends to answer how strong the interest in small area estimation is, what the demand for practical and theoretical knowledge in the field is and what the recommendations for universities and statistical institutes are.
Small Area Prediction Under Alternative Model Specifications
Authors: Erciulescu Andreea L. , Fuller Wayne A. 
Published: November 2017

Abstract
Construction of small area predictors and estimation of the prediction mean squared error, given different types of auxiliary information are illustrated for a unit level model. Of interest are situations where the mean and variance of an auxiliary variable are subject to estimation error. Fixed and random specifications for the auxiliary variables are considered. The efficiency gains associated with the random specification for the auxiliary variable measured with error are demonstrated. A parametric bootstrap procedure is proposed for the mean squared error of the predictor based on a logit model. The proposed bootstrap procedure has smaller bootstrap error than a classical double bootstrap procedure with the same number of samples.
A Comparison of Small Area Estimation Methods for Poverty Mapping
Authors: Guadarrama María , Molina Isabel , Rao J. N. K. 
Published: November 2017

Abstract
We review main small area estimation methods for the estimation of general non-linear parameters focusing on FGT family of poverty indicators introduced by Foster, Greer and Thorbecke (1984). In particular, we consider direct estimation, the Fay-Herriot area level model (Fay and Herriot, 1979), the method of Elbers, Lanjouw and Lanjouw (2003) used by the World Bank, the empirical Best/Bayes (EB) method of Molina and Rao (2010) and its extension, the Census EB, and finally the hierarchical Bayes proposal of Molina, Nandram and Rao (2014). We put ourselves in the point of view of a practitioner and discuss, as objectively as possible, the benefits and drawbacks of each method, illustrating some of them through simulation studies.
A Two-Component Normal Mixture Alternative to the Fay-Herriot Model
Authors: Chakraborty Adrijo , Datta Gauri Sankar , Mandal Abhyuday 
Published: November 2017

Abstract
This article considers a robust hierarchical Bayesian approach to deal with random effects of small area means when some of these effects assume extreme values, resulting in outliers. In the presence of outliers, the standard Fay-Herriot model, used for modeling area-level data, under normality assumptions of random effects may overestimate the random effects variance, thus providing less than ideal shrinkage towards the synthetic regression predictions and inhibiting the borrowing of information. Even a small number of substantive outliers of random effects results in a large estimate of the random effects variance in the Fay-Herriot model, thereby achieving little shrinkage to the synthetic part of the model or little reduction in the posterior variance associated with the regular Bayes estimator for any of the small areas. While the scale mixture of normal distributions with a known mixing distribution for the random effects has been found to be effective in the presence of outliers, the solution depends on the mixing distribution. As a possible alternative solution to the problem, a two-component normal mixture model has been proposed, based on non-informative priors on the model variance parameters, regression coefficients and the mixing probability. Data analysis and simulation studies based on real, simulated and synthetic data show an advantage of the proposed method over the standard Bayesian Fay-Herriot solution derived under normality of random effects.
Variational Approximations for Selecting Hierarchical Models of Circular Data in a Small Area Estimation Application
Authors: Hernandez-Stumpfhauser Daniel , Breidt F. Jay , Opsomer Jean D. 
Published: November 2017

Abstract
We consider hierarchical regression models for circular data using the projected normal distribution, applied in the development of weights for the Access Point Angler Intercept Survey, a recreational angling survey conducted by the US National Marine Fisheries Service. Weighted estimates of recreational fish catch are used in stock assessments and fisheries regulation. The construction of the survey weights requires the distribution of daily departure times of anglers from fishing sites, within spatio-temporal domains subdivided by the mode of fishing. Because many of these domains have small sample sizes, small area estimation methods are developed. Bayesian inference for the circular distributions on the 24-hour clock is conducted, based on a large set of observed daily departure times from another National Marine Fisheries Service study, the Coastal Household Telephone Survey. A novel variational/Laplace approximation to the posterior distribution allows fast comparison of a large number of models in this context, by dramatically speeding up computations relative to the fast Markov Chain Monte Carlo method while giving virtually identical results.
A Comparison of Small Area and Calibration Estimators Via Simulation
Authors: Hidiroglou M. A. , Estevao V. M. 
Published: November 2017

Abstract
Domain estimates are typically obtained using calibration estimators that are direct or modified direct. They are direct if they strictly use data within the domain of interest. They are modified direct if they use both data within and outside the domain of interest. An alternative way of producing these estimates is through small area procedures. In this article, we compare the performance of these two approaches via a simulation. The population is generated using a hierarchical model that includes both area effects and unit level random errors. The population is made up of mutually exclusive domains of different sizes, ranging from a small number of units to a large number of units. We select many independent simple random samples of fixed size from the population and compute various estimates for each sample using the available auxiliary information. The estimates computed for the simulation included the Horvitz-Thompson estimator, the synthetic estimator (indirect estimate), calibration estimators, and unit level based estimators (small area estimate). The performance of these estimators is summarized based on their design-based properties.
Quality of Life and Poverty in Ukraine – Preliminary Assessment Based on the Subjective Well-Being Indicators 1
Authors: Osaulenko Oleksandr 
Published: November 2017

Abstract
The paper provides an overview of the information sources, methodology and main findings of the research of quality of life and poverty using indicators of subjective well-being applied by state statistics agencies in Ukraine. The paper describes the system of indicators for self-evaluation of the attained level of well-being, the level of satisfaction from meeting the basic living needs, and the limitations in consumption abilities of selected population groups due to hard conditions. In addition, methodological approaches in national statistics practice are discussed for the case of analysis of economic deprivation and for infrastructure development as indicator of geographic accessibility of services and non-geographic barriers causing the deprivation of access. Also, this paper reviews the factors that underlie the deprivations and define the percentage of population that is particularly affected by multiple deprivation in Ukraine. It covers the data on dynamics and analyses the distribution of deprivation by different population group, for several years. Finally, it describes further steps on the way to enhance the information capacity of subjective wellbeing studies, particularly as regards implementation of the contemporary approaches in international perspective, including Europe.
Knowledge-Based Economy in the European Union – Cross-Country Analysis
Authors: Skrodzka Iwona 
Published: November 2017

Abstract
The knowledge-based economy is an economy where knowledge is created, acquired, transmitted and used effectively by businesses, organizations, individuals and communities. It is not narrowly focused on the industries of advanced technology or ICT, but provides a framework for analysing the range of policy options in education, information infrastructure and systems of innovation, which could help contribute to the knowledge economy. The aim of the paper is to analyse spatial differences in the level of development of the knowledge-based economy in the European Union countries. The study uses a soft modelling method, which enables the estimation of a synthetic measure of KBE as well as the arrangement and classification of the UE-27 countries into typological groups. The research covers the years 2000 and 2013.
The Gluevar Risk Measure and Investor’s Attitudes to Risk–An Application to the Non-Ferrous Metals Market
Authors: Krężołek Dominik 
Published: November 2017

Abstract
Investing in the economic world, characterized by a high level of uncertainty and volatility, entails a higher level of risk related to investment. One of the most commonly used risk measure is Value-at-Risk. However, despite the ease of calculation and interpretation, this measure suffers from a significant drawback - it is not subadditive. This property is the key issue in terms of portfolio diversification. Another risk measure, which meets this assumption, has been proposed - Conditional Value-at-Risk, defined as a conditional loss beyond Value-at-Risk. However, the choice of a risk measure is an individual decision of an investor and it is directly related to his attitudes to risk.
In this paper the new risk measure is proposed - the GlueVaR risk measure, which can be defined as a linear combination of VaR and GlueVaR. It allows for calculating the level of investment loss depending on investment’s attitudes to risk. Moreover, GlueVaR meets the subadditivity property, therefore it may be used in portfolio risk assessment. The application of the GlueVaR risk measure is presented for the non-ferrous metals market.
Examining Similarities in Time Allocation Amongst European Countries
Authors: Hozer-Koćmiel Marta , Lis Christian 
Published: November 2017

Abstract
The aim of the article is to analyse the similarities between the selected European countries in terms of time allocation. Time allocation has been defined as the daily distribution of time to various activities. Professional work time, domestic work time and leisure time are the most important for the economic approach. It has been proved that there are coherent groups of countries with similar structure of time allocation. The taxonomic methods used in order to verify the thesis included: cluster analysis, k-means method, generalised distance measure GDM and interval taxonomic method TMI. The analysis was performed on the basis of HETUS data.
Quality of Institutions and Total Factor Productivity in the European Union
Authors: Balcerzak Adam P. , Pietrzak Michał Bernard 
Published: November 2017

Abstract
The key challenge for mid- and long-term policy in the European Union countries is to use the potentials of knowledge-based economy (KBE), which is a condition for maintaining high total factor productivity in Europe. For this reason, the relationship between the quality of an institutional system and total factor productivity in the EU countries has been examined. The quality of the institutional system is defined here from the perspective of incentives that influence the use of the potential of KBE. In order to determine the level of effectiveness of the institutional system in the analysed countries the method for linear ordering of objects was applied based on data from Fraser Institute. The main hypothesis of the article states that the quality of the institutional system in the context of KBE has a significant influence on the level of total factor productivity in the EU. In order to verify this hypothesis, the parameters of the Cobb-Douglas production function were estimated, which allowed the evaluation of TFP for the EU countries. The calculation made in the article based on Eurostat data. In order to identifying the relationship between the quality of the institutional system and the level of TFP a panel model was applied using data from a conducted for years 2000-2010.
Locally Regularized Linear Regression in the Valuation of Real Estate
Authors: Kubus Mariusz 
Published: November 2017

Abstract
Regression methods are used for the valuation of real estate in the comparative approach. The basis for the valuation is a data set of similar properties, for which sales transactions were concluded within a short period of time. Large and standardized databases, which meet the requirements of the Polish Financial Supervision Authority, are created in Poland and used by the banks involved in mortgage lending, for example.
We assume that in the case of large data sets of transactions, it is more advantageous to build local regression models than a global model. Additionally, we propose a local feature selection via regularization. The empirical research carried out on three data sets from real estate market confirmed the effectiveness of this approach. We paid special attention to the model quality assessment using cross-validation for estimation of the residual standard error.
Kernel Estimation of Cumulative Distribution Function of a Random Variable with Bounded Support
Authors: Baszczyńska Aleksandra 
Published: November 2017

Abstract
In the paper methods of reducing the so-called boundary effects, which appear in the estimation of certain functional characteristics of a random variable with bounded support, are discussed. The methods of the cumulative distribution function estimation, in particular the kernel method, as well as the phenomenon of increased bias estimation in boundary region are presented. Using simulation methods, the properties of the modified kernel estimator of the distribution function are investigated and an attempt to compare the classical and the modified estimators is made.
Shift in Methodology and Population Census Quality
Authors: Gołata Elżbieta 
Published: November 2017

Abstract
The article refers to the shift in methods to conduct a population census: from a conventional enumeration through a sample survey and a mixed approach to administrative data, as a new standard in statistics. The paper compares two Polish censuses of 2002 and 2011. It is aimed at quality assessment in the case of both: the traditional method (2002 census) and the combined approach (2011 census).
The quality of census data is discussed with essential aims and objectives to provide reliable information on the population age and sex structure in detailed territorial division. Therefore, quality assessment is provided for the whole country and at regional level. First of all, coverage errors are considered. We use multiple sources of data and non-matching methods, in particular: demographic analysis based on previous censuses, vital statistics and a comparison with other existing sources. Different cross-sections according to sex, age and place of residence are considered. In each separate domain adequacy and divergence assessments are accompanied by an attempt to provide substantive explanations.
The Identification of Training Needs for Human Capital Quality Improvement in Poland – A Statistical Approach
Authors: Dziechciarz Duda Marta , Dziechciarz Jozef 
Published: November 2017

Abstract
The Ministry of Science and Higher Education has launched the Competency Development Programme in the form of additional financial means for activities to equip students with unique, the so-called soft skills necessary in scientific careers and on the labour market. Courses developing skills such as team work ability, leadership, creativity, independent thinking and innovative approach to problem solving will be financed.
For that purpose, a thorough analysis of needs is necessary. Existing databases describing the quality of human capital in Poland should be analysed in order to identify those competencies that graduates of universities are missing.
The arsenal of possible statistical tools applicable for that purpose covers a wide range of techniques, from the simplest methods of descriptive statistics to advanced multivariate statistical analysis.
The study will attempt to identify the missing soft competences based on existing statistical data, e.g. Polish human capital database.
M-Estimators in Business Statistics1
Authors: Dehnel Grażyna 
Published: November 2017

Abstract
Recent years have seen a dynamic development in statistical methods for analysing data contaminated with outliers. One of the more important techniques that can deal with outlying observations is robust regression, which represents four decades of research. Until recently the implementation of robust regression methods, such as M-estimation or MM-estimation, was limited owing to their iterative nature. With advances in computing power and the growing availability of statistical packages, such as R and SAS, Stata, the applicability of robust regression methods has increased considerably.The aim of the study is to evaluate one of these methods, namely M-estimation, using data from a survey of small and medium-sized businesses. The comparison involves nine M-estimators, each based on a different weighting function. The results and conclusions are formulated on the basis of empirical data from the DG-1 business survey.
The Challenges of the Population Census Round of 2020. Outline of the Methods of Quality Assessment of Population Census Data1
Authors: Kordos Jan 
Published: November 2017

Abstract
The author begins with the challenges of the population census round of 2020 and gives an outline of the methods of quality assessment of population census data. Next, the synthesis of two Eurostat documents (2007, 2009) and UN Statistics Division (2010) monograph relating to census data quality assessment are considered. Special attention is paid to three methods of census quality assessment: (i) demographic method, (ii) post-enumeration survey, and (iii) comparison with existing household surveys. Census quality assessment methods are discussed in the context of the international recommendations and experience of selected countries. Some Polish experience in these fields is also discussed, and some suggestions to the 2021 Polish Census of Population and Housing preparations are given.