bluuebunny/crossref_metadata_2025_split huggingface dataset:
Classification Problems Based on Regression Models for Multi-Dimensional Functional Data
Authors: Górecki Tomasz , Krzyśko Mirosław , Wołyński Waldemar 
Published: November 2017

AbstractData in the form of a continuous vector function on a given interval are referred to as multivariate functional data. These data are treated as realizations of multivariate random processes. We use multivariate functional regression techniques for the classification of multivariate functional data. The approaches discussed are illustrated with an application to two real data sets.
An Approximation to the Optimal Subsample Allocation for Small Areas
Authors: Molefe W. B. , Shangodoyin D. K. , Clark R. G. 
Published: November 2017

Abstract
This paper develops allocation methods for stratified sample surveys in which small area estimation is a priority. We assume stratified sampling with small areas as the strata. Similar to Longford (2006), we seek efficient allocation that minimizes a linear combination of the mean squared errors of composite small area estimators and of an estimator of the overall mean. Unlike Longford, we define mean-squared error in a model-assisted framework, allowing a more natural interpretation of results using an intra-class correlation parameter. This allocation has an analytical form for a special case, and has the unappealing property that some strata may be allocated no sample. We derive a Taylor approximation to the stratum sample sizes for small area estimation using composite estimation giving priority to both small area and national estimation.
A Measure for Regional Resilience to Economic Crisis1
Authors: Markowska Małgorzata 
Published: November 2017

Abstract
The purpose of the study (presented in this article) was to develop a measure of resilience to crisis, one that may be applied to regional data. In principle, such measure can take either positive or negative values. A positive value confirms resilience to crisis, whereas a negative one confirms the absence of resilience (sensitivity/vulnerability). The measure uses growth rates referred to the previous year under the assumption that crisis results in a slowdown in growth, or even in a decline in values of important economic indicators. Growth rates are standardized by dividing values of original change rates by medians specified based on spatio-temporal data modules. Such division results in each characteristic being brought to equal validity. Simultaneously, the original character is maintained and variables are not “flattened” by the outliers. Changing destimulants into stimulants occurs during growth rates calculation. The measure of resilience to crisis is calculated as an arithmetic mean of the values of characteristics brought to comparability. The measure of resilience can be converted into the measure of sensitivity by multiplying it by (-1).
The application of the proposed measure to assessing the resilience to crisis in the period 2006-2011 is presented for regions meant as the European Union NUTS2 units. The measure is based on comparable data, which allowed for using only six variables measuring changes in GDP, salaries, investments, household income, employment and unemployment.
Exploiting Ordinal Data for Subjective Well-Being Evaluation
Authors: Fattore Marco , Maggino Filomena , Arcagni Alberto 
Published: November 2017

Abstract
The evaluation of subjective well-being, and of similar issues related to quality of life, is usually addressed through composite indicators or counting procedures. This leads to inconsistencies and inefficiency in the treatment of ordinal data that, in turn, affect the quality of information provided to scholars and to policy-makers. In this paper we take a different path and prove that the evaluation of multidimensional ordinal well-being can be addressed in an effective and consistent way, using the theory of partially ordered sets. We first show that the proper evaluation space of well-being is the partially ordered set of achievement profiles and that its structure depends upon the importance assigned to well-being attributes. We then describe how evaluation can be performed extracting information out of the evaluation space, respecting the ordinal nature of data and producing synthetic indicators without attribute aggregation. An application to subjective well-being in Italy illustrates the procedure.
On Measuring Income Polarization: An Approach Based on Regression Trees
Authors: Mussini Mauro 
Published: November 2017

Abstract
This article proposes the application of regression trees for analysing income polarization. Using an approach to polarization based on the analysis of variance, we show that regression trees can uncover groups of homogeneous income receivers in a data-driven way. The regression tree can deal with nonlinear relationships between income and the characteristics of income receivers, and it can detect which characteristics and their interactions actually play a role in explaining income polarization. For these features, the regression tree is a flexible statistical tool to explore whether income receivers concentrate around local poles. An application to Italian individual income data shows an interesting partition of income receivers.
New Method of Variable Selection for Binary Data Cluster Analysis
Authors: Korzeniewski Jerzy 
Published: November 2017

Abstract
Cluster analysis of binary data is a relatively poorly developed task in comparison with cluster analysis for data measured on stronger scales. For example, at the stage of variable selection one can use many methods arranged for arbitrary measurement scales but the results are usually of poor quality. In practice, the only methods dedicated for variable selection for binary data are the ones proposed by Brusco (2004), Dash et al. (2000) and Talavera (2000). In this paper the efficiency of these methods will be discussed with reference to the marketing type data of Dimitriadou et al. (2002). Moreover, the primary objective is a new proposal of variable selection method based on connecting the filtering of the input set of all variables with grouping of sets of variables similar with respect to similar groupings of objects. The new method is an attempt to link good features of two entirely different approaches to variable selection in cluster analysis, i.e. filtering methods and wrapper methods. The new method of variable selection returns best results when the classical k-means method of objects grouping is slightly modified.
Sujatha Distribution and its Applications
Authors: Shanker Rama 
Published: November 2017

AbstractIn this paper a new one-parameter lifetime distribution named “Sujatha Distribution” with an increasing hazard rate for modelling lifetime data has been suggested. Its first four moments about origin and moments about mean have been obtained and expressions for coefficient of variation, skewness, kurtosis and index of dispersion have been given. Various mathematical and statistical properties of the proposed distribution including its hazard rate function, mean residual life function, stochastic ordering, mean deviations, Bonferroni and Lorenz curves, and stress-strength reliability have been discussed. Estimation of its parameter has been discussed using the method of maximum likelihood and the method of moments. The applications and goodness of fit of the distribution have been discussed with three real lifetime data sets and the fit has been compared with one-parameter lifetime distributions including Akash, Shanker, Lindley and exponential distributions.
Estimation of Mean on the Basis of Conditional Simple Random Sample
Authors: Wywiał Janusz 
Published: November 2017

AbstractEstimation of the population mean in a finite and fixed population on the basis of the conditional simple random sampling design dependent on order statistics (quantiles) of an auxiliary variable is considered. Properties of the well-known Horvitz-Thompson and ratio type estimators as well as the sample mean are taken into account under the conditional simple random sampling designs. The considered examples of empirical analysis lead to the conclusion that under some additional conditions the proposed estimation strategies based on the conditional simple random sample are usually more accurate than the mean from the simple random sample drawn without replacement.
Prediction of a Function of Misclassified Binary Data
Authors: Al-Kandari Noriah M. , Lahiri Partha 
Published: November 2017

Abstract
We consider the problem of predicting a function of misclassified binary variables. We make an interesting observation that the naive predictor, which ignores the misclassification errors, is unbiased even if the total misclassification error is high as long as the probabilities of false positives and false negatives are identical. Other than this case, the bias of the naive predictor depends on the misclassification distribution and the magnitude of the bias can be high in certain cases. We correct the bias of the naive predictor using a double sampling idea where both inaccurate and accurate measurements are taken on the binary variable for all the units of a sample drawn from the original data using a probability sampling scheme. Using this additional information and design-based sample survey theory, we derive a bias-corrected predictor. We examine the cases where the new bias-corrected predictors can also improve over the naive predictor in terms of mean square error (MSE).
Changing Mortality Distribution in Developed Countries from 1970 to 2010: Looking at Averages and Beyond Them
Authors: Szulc Adam 
Published: November 2017

Abstract
The methods typically developed in income inequality and poverty research are employed to observe changes in life spans distribution in 35 developed countries. The analyses are performed at two levels, using the same methods when possible: i/ taking the countries as the units with a mean length of life being a single parameter representing the distribution, ii/ utilizing the country life tables (taking people as the units) in order to compare other than mean length of life attributes of mortality distribution. Increasing divergence in the mean length of life across the countries is due to growing distance of the countries below the median, mainly the post-communist ones, to the upper half. The comparisons of the within-country distributions of ages at death by means of the Kullback-Leibler divergence provides similar results. However, poverty and inequality indices calculated at this level yield opposite conclusions. Hence, most of the between-country variation might be attributed to the variation in the mean length of life while the changes in within-country inequality reduced this effect. At the same time, huge alterations in the within-country mortality rankings can be observed. Australia, Japan, Taiwan, Austria and Luxembourg may be said to be the “winners” while most of the post-communist countries are among the “losers”.
Spatial Autocorrelation in Assessment of Financial Self-Sufficiency of Communes of Wielkopolska Province
Authors: Kozera Agnieszka , Głowicka-Wołoszyn Romana 
Published: November 2017

Abstract
The aim of the article was to identify the spatial effects in assessment of financial self-sufficiency of the governments of communes (gminas) of Wielkopolska province (voivodship) in 2014, employing global and local Moran I statistics. The level of the governments’ self-sufficiency was examined by positional TOPSIS method. The study was based on publicly accessible databases compiled by the Ministry of Finance (Wskaźniki do oceny sytuacji finansowej jednostek samorządu terytorialnego) and the Central Statistical Office (Local Data Bank). Calculations were performed in R with packages spdep, maptools and shapefiles.
The study demonstrated that the communes of Wielkopolska province of comparable levels of financial self-sufficiency exhibited a moderate tendency to cluster. Clusters of high levels gathered around larger urban centres, especially around Poznań, while clusters of low levels – in economically underdeveloped agricultural south-eastern and northern part of the province.
In Search of Hedges and Safe Havens in Global Financial Markets
Authors: Wanat Stanisław , Śmiech Sławomir , Papież Monika 
Published: November 2017

Abstract
The aim of the paper is to search for hedges and safe havens within three instrument classes: assets (represented by the S&P500 index), gold and oil prices, and dollar exchange rates. Weekly series of returns of all the instruments from the period January 1995 – June 2015 are analysed. The study is based on conditional correlations between the instruments in different market regimes obtained with the use of copula-DCC GARCH models. It is assumed that different market regimes will be identified by statistical clustering techniques; however, only conditional variances (without conditional covariances) will be taken into account. The reason for this assumption is connected with the fact that variances can be understood as market risk, and, as such, are a good indicator of market conditions. A considerable advantage of such an approach is the lack of need to determine the number of market regimes, as it is established by clustering quality measures. What is more, the methodology used in the paper makes it possible to treat the relations between instruments symmetrically. The results obtained in the study reveal that only dollar exchange rates can be treated as a (strong) hedge and a (strong) safe haven for other instruments, while gold and oil are a hedge for assets.
Bayesian Accelerated Failure Time and its Application in Chemotherapy Drug Treatment Trial
Authors: Prabhash Kumar , Patil Vijay M , Noronha Vanita , Joshi Amit , Bhattacharjee Atanu 
Published: November 2017

Abstract
The Cox proportional hazards model (CPH) is normally applied in clinical trial data analysis, but it can generate severe problems with breaking the proportion hazard assumption. An accelerated failure time (AFT) is considered as an alternative to the proportional hazard model. The model can be used through consideration of different covariates of interest and random effects in each section. The model is simple to fit by using OpenBugs software and is revealed to be a good fit to the Chemotherapy data.
Calendar and Seasonal Effects on the Size of Withdrawals from Atms Managed By Euronet
Authors: Gurgul Henryk , Suder Marcin 
Published: November 2017

Abstract
This study analyses the calendar effects on withdrawals from Automated Teller Machines (ATMs) (daily data) managed by the Euronet network for the period from January 2008 to March 2012. Our study focuses on the identification of specific calendar and seasonal effects in the ATM cash withdrawal series of the company in the Polish provinces of Małopolska and Podkarpackie. The results of the analysis show that withdrawals depend strongly on the day of the week. On Fridays more cash is withdrawn than on other days, and Saturdays and Sundays are the days of the week with the lowest level of withdrawals. In a month, it can be seen that cash withdrawals take place more often in the second and in the last weeks of the month. This observation suggests that withdrawals from ATMs can be related to the profile of wage withdrawals. In Poland, in the public sector wages are paid at the beginning of the month, and in the private sector at the end of the month. The time series of withdrawals also reflect seasonality. The largest amounts are withdrawn in July, August and December. Reason for the increased demand for cash are the summer holidays and the Christmas season. The results reflect consumer habits which show pronounced calendar and seasonal effects.
Interval Estimation of Higher Order Quantiles. Analysis of Accuracy of Selected Procedures
Authors: Pekasiewicz Dorota 
Published: November 2017

Abstract
In the paper selected nonparametric and semiparametric estimation methods of higher orders quantiles are considered. The construction of nonparametric confidence intervals is based on order statistics of appropriate ranks from random samples or from generated bootstrap samples. Semiparametric bootstrap methods are characterized by double bootstrap simulations. The values of bootstrap sample below the prearranged threshold are generated by the empirical distribution and the values above this threshold are generated by the distribution based on the asymptotic properties of the tail of the random variable distribution. The results of the study allow one to draw conclusions about the effectiveness of the considered procedures and to compare these methods.
Informative Versus Non-Informative Prior Distributions and their Impact on the Accuracy of Bayesian Inference
Authors: Grzenda Wioletta 
Published: November 2017

Abstract
In this study the benefits arising from the use of the Bayesian approach to predictive modelling will be outlined and exemplified by a linear regression model and a logistic regression model. The impact of informative and non-informative prior on model accuracy will be examined and compared. The data from the Central Statistical Office of Poland describing unemployment in individual districts in Poland will be used. Markov Chain Monte Carlo methods (MCMC) will be employed in modelling.
The Application of Multivariate Statistical Analysis to the Valuation of Durable Goods Brands
Authors: Dziechciarz-Duda Marta , Król Anna 
Published: November 2017

Abstract
Nowadays, due to changes in the market and new trends in consumer behaviours, intangible assets, such as brand, have gained fundamental importance. The more frequent conviction that a product with a well-known name is better than other products contributes to the case of replacing the price of a product by its brand name as the predominant factor in the purchase decision process. Thus, for many companies the strengthening of brand equity has become one of the key elements of marketing strategy.
The main aim of this study is an attempt to improve the process of analysing the position and value of brands using selected multivariate statistical analysis methods (hedonic regression, multidimensional scaling, classification and linear ordination methods). In the conducted research the direct approach to the evaluation of the position of the brands for a selected ICT good – smartphones – have been applied. The measurement was performed on two levels: the product level, in which the prices of branded products were compared, and the consumer level, where the perception and attitudes of consumers towards the brands were studied. The analyses have been carried out on two sets of data, which enabled fuller and more comprehensible understanding of decision rules that guide consumers in choosing the brand.
Trade Pattern on Warsaw Stock Exchange and Prediction of Number of Trades
Authors: Gurgul Henryk , Machno Artur 
Published: November 2017

Abstract
The main goal of this paper is to present the method for describing and predicting trade intensity on the Warsaw Stock Exchange. The approach is based on generalized linear models, the variable selection is performed using shrinkage methods such as the Lasso or Ridge regression. The variable under investigation is the number of trades of a particular stock 5-minute interval.
The main conclusion is that the number of trades during short intervals is predictable in the sense that the prediction, even based on relatively simple models, is with respect to statistical properties better than the prediction based on the random walk, which is used as a benchmark model.
The Application of Bűhlmann-Straub Model to the Estimation of Net Premium Rates Depending on the Age of the Insured in the Motor Third Liability Insurance
Authors: Szymańska Anna 
Published: November 2017

Abstract
One of the basic variables used in the process of tariff calculation of premiums in motor liability insurance is the age of the insured. In this type of insurance offered by insurers operating on the Polish market, this variable is taken into account in the ratemaking by discounts and increases in assigned premium, known as the net premiums rates. The aim of this work is to propose a method of rate estimation of net premiums in the groups of the motor third liability insurance portfolio of individuals created by the age of the insured. For the premium estimation, one of the maximum likelihood models, called the Bűhlmann-Straub model, was used.
Residency Testing. Estimating the True Population Size of Estonia
Authors: Tiit Ene-Margit 
Published: November 2017

Abstract
The number of residents or population size is important for all countries. Nowadays in many countries a series of registers have been created, which can be used for assessing the population size. The residency index is a tool created for estimating the under- and over-coverage of population census and calculation of proper population size. For this aim the concept of a sign of life – a binary variable depending on register i, person j and year k has been introduced showing if the person was active in the register in a given year. The weighted sum of signs of life indicates the probability that the person belongs to the set of residents in a given year. To improve the stability of the index a linear combination of the previous value of the index and the sum of signs of life is used. Necessary parameters were estimated using empirical data.
Efficient Family of Ratio-Type Estimators for Mean Estimation in Successive Sampling on two Occasions Using Auxiliary Information
Authors: Beevi Nazeema T. , Chandran C. 
Published: November 2017

Abstract
In this paper, we proposed an efficient family of ratio-type estimators using one auxiliary variable for the estimation of the current population mean under successive sampling scheme. This family of estimators have been studied by Ray and Sahai (1980) under simple random sampling using one auxiliary variable for estimation of the population mean. Using these estimators in successive sampling, the expression for bias and mean squared error of the proposed estimators are obtained up to the first order of approximation. Usual ratio estimator is identified as a particular case of the suggested estimators. Optimum replacement strategy is also discussed. The proposed family of estimators at optimum condition is compared with the simple mean per unit estimator, Cochran (1977) estimator and existing other members of the family. Expressions of optimization are derived and results have been justified through numerical study interpretation.
Met and Unmet Need for Contraception: Small Area Estimation for Rajasthan State of India
Authors: Rai Piyush Kant , Pareek Sarla , Joshi Hemlata 
Published: November 2017

Abstract
Nowadays, Family Planning has become the basic human right, which is closely linked to the empowerment of women and perhaps it is the only treatment that can avert many serious issues, which are an impediment in the advancement of the country like maternal mortality, infant mortality and can exert out the families from poverty and stabilize population growth etc. Increasing use of family planning can be helpful in the reduction of unmet need for family planning by which a substantial proportion of unwanted births ends in childbirths, and which are related to deaths and injuries for both mother and child.
Due to lack of availability of reliable data at the small level (area-wise) specifically in developing countries like India. In this article the small area estimation technique is used for the estimation of met and unmet need for contraception for 187 towns of Rajasthan state of India and for empirical analysis. Data is taken from the District Level Household Survey (DLHS): 2002-04 and the Census 2001 of India.