bluuebunny/crossref_metadata_2025_split huggingface dataset:
New improved Poisson and negative binomial item count techniques for eliciting truthful answers to sensitive questions
Authors: Kowalczyk Barbara , Wieczorkowski Robert 
Published: October 2022

Abstract
Item count techniques (ICTs) are indirect survey questioning methods designed to deal with sensitive features. These techniques have gained the support of many applied researchers and undergone further theoretical development. Latterly in the literature, two new item count methods, called Poisson and negative binomial ICTs, have been proposed. However, if the population parameters of the control variable are not provided by the outside source, the methods are not very efficient. Efficiency is an important issue in indirect methods of questioning due to the fact that the protection of respondents’ privacy is usually achieved at the expense of the efficiency of the estimation. In the present paper we propose new improved Poisson and negative binomial ICTs, in which two control variables are used in both groups, although in a different manner. In the paper we analyse best linear unbiased and maximum likelihood estimators of the proportion of the sensitive attribute in the population in the introduced new models. The theoretical findings presented in the paper are supported by a comprehensive simulation study. The improved procedure allowed the increase of the efficiency of the estimation compared to the original Poisson and negative binomial ICTs.
Long-term sovereign interest rates in Czechia, Hungary and Poland: a comparative assessment with an affine term structure model
Authors: Janus Jakub 
Published: October 2022

Abstract
This paper provides a comparative evaluation of the behaviour of long-term sovereign yields in Czechia, Hungary and Poland from 2001 to 2019. An affine term structure model developed by Adrian, Crump and Moench (2013) is used as an empirical framework for the decomposition of the bond yields into term premium and risk-neutral components. We document a substantial compression in term premia which started in Central European economies around 2013 and played a decisive role in the changes that occurred in 10-year sovereign yields. This pattern, however, was more prevalent in Czechia and Poland than in Hungary. We show that long-term rates in all three economies remained higher than in Germany due to relatively large risk-neutral components. Nevertheless, cross-country correlations became increasingly dependent on term premium dynamics, both among Central European economies and between each of them and Germany. These results are robust to bias-correction in the baseline models and interpreted in the light of the general interest rates decline in the global economy. Potential policy implications are also discussed.
Variance estimation in stratified adaptive cluster sampling
Authors: Yasmeen Uzma , Noor-ul-Amin Muhammad , Hanif Muhammad 
Published: October 2022

Abstract
In many sampling surveys, the use of auxiliary information at either the design or estimation stage, or at both these stages is usual practice. Auxiliary information is commonly used to obtain improved designs and to achieve a high level of precision in the estimation of population density. Adaptive cluster sampling (ACS) was proposed to observe rare units with the purpose of obtaining highly precise estimations of rare and specially clustered populations in terms of least variances of the estimators. This sampling design proved to be more precise than its more conventional counterparts, including simple random sampling (SRS), stratified sampling, etc. In this paper, a generalised estimator is anticipated for a finite population variance with the use of information of an auxiliary variable under stratified adaptive cluster sampling (SACS). The bias and mean square error expressions of the recommended estimators are derived up to the first degree of approximation. A simulation study showed that the proposed estimators have the least estimated mean square error under the SACS technique in comparison to variance estimators in stratified sampling.
Interval Type-2 fuzzy Exponentially Weighted Moving Average Control Chart
Authors: Adepoju Akeem Ajibola , Abdulkadir Sauta S. , Jibasen Danjuma , Chiroma Haruna 
Published: October 2022

Abstract
Some industrial data often come with uncertainty, which in some cases depends on the decision of those responsible for taking the measurement in the production process. While the fuzzy approach helps to tackle the ambiguity that arises in the measurement, an interval type-2 fuzzy set deals with such uncertainty better due to its flexibility over the control limits of its control chart. This paper aims to develop an Interval Type-2 fuzzy Exponentially Weighted Moving Average Control Chart (IT2FEWMA) under the fuzzy type-2 condition. This development will facilitate monitoring small and moderate shifts in the production process in conditions of uncertainty.
The length-biased power hazard rate distribution: Some properties and applications
Authors: Mustafa Abdelfattah , Khan M. I. 
Published: October 2022

Abstract
In this article, the length-biased power hazard rate distribution has introduced and investigated several statistical properties. This distribution reports an extension of several probability distributions, namely: exponential, Rayleigh, Weibull, and linear hazard rate. The procedure of maximum likelihood estimation is taken for parameters. Finally, the applicability of the model is explored by three real data sets. To examine, the performance of the technique, a simulation study is extracted.
Socio-economic development and quality of life of NUTS-2 units in the European Union
Authors: Jewczak Maciej , Brudz Magdalena 
Published: October 2022

Abstract
Analyses regarding socio-economic development and quality of life are an important aspect of research and discussion for many international organisations, states and local authorities. Due to the complexity and multidimensionality of these issues, conducting research can be problematic. The conclusions of various analytical centres indicate that there are many paths towards establishing a set of factors which affect quality of life and ways of assessing socio-economic development levels. Depending on the criteria considered, the most common methods for determining the degree of the advancement of life quality or socio-economic development include taxonomical techniques and analyses of potential, which are based mainly on objective data sourced from official registers.
The main purpose of the paper is to investigate the level of socio-economic development and quality of life in the European Union in the years 2004 and 2018. The analyses were conducted for a rarely used level of spatial data aggregation, i.e. for NUTS-2 units. The analysis covers only those European regions that were EU members in 2004. As the primary research tool, the two-dimensional development matrix was adopted, which enabled the verification of the hypothesis regarding the convergence of synthetic measures that indicate the levels of socio-economic development and quality of life in the EU regions. For these indices, the development matrix is also used to identify the strengths and weaknesses as well as the opportunities and threats for selected spatial units, and, at the same time, to estimate the rates of change of the socio-economic development and quality of life levels.
ARFURIMA models: simulations of their properties and application
Authors: Jibrin Sanusi Alhaji , Rahman Rosmanjawati Abdul 
Published: October 2022

AbstractThis article defines the Autoregressive Fractional Unit Root Integrated Moving Average (ARFURIMA) model for modelling ILM time series with fractional difference value in the interval of 1 <d< 2. The performance of the ARFURIMA model is examined through a Monte Carlo simulation. Also, some applications were presented using the energy series, bitcoin exchange rates and some financial data to compare the performance of the ARFURIMA and the Semiparametric Fractional Autoregressive Moving Average (SEMIFARMA) models. Findings showed that the ARFURIMA outperformed the SEMIFARMA model. The study’s conclusion provides another perspective in analysing large time series data for modelling and forecasting, and the findings suggest that the ARFURIMA model should be applied if the studied data show a type of ILM process with a degree of fractional difference in the interval of 1 <d< 2.
Extracting relevant predictors of the severity of mental illnesses from clinical information using regularisation regression models
Authors: Kaushik Sakshi , Sabharwal Alka , Grover Gurprit 
Published: October 2022

Abstract
Mental disorders are common non-communicable diseases whose occurrence rises at epidemic rates globally. The determination of the severity of a mental illness has important clinical implications and it serves as a prognostic factor for effective intervention planning and management. This paper aims to identify the relevant predictors of the severity of mental illnesses (measured by psychiatric rating scales) from a wide range of clinical variables consisting of information on both laboratory test results and psychiatric factors. The laboratory test results collectively indicate the measurements of 23 components derived from vital signs and blood tests results for the evaluation of the complete blood count. The 8 psychiatric factors known to affect the severity of mental illnesses are considered, viz. the family history, course and onset of an illness, etc. Retrospective data of 78 patients diagnosed with mental and behavioural disorders were collected from the Lady Hardinge Medical College & Smt. S.K, Hospital in New Delhi, India. The observations missing in the data are imputed using the non-parametric random forest algorithm. The multicollinearity is detected based on the variance inflation factor. Owing to the presence of multicollinearity, regularisation techniques such as ridge regression and extensions of the least absolute shrinkage and selection operator (LASSO), viz. adaptive and group LASSO are used for fitting the regression model. Optimal tuning parameter λ is obtained through 13-fold cross-validation. It was observed that the coefficients of the quantitative predictors extracted by the adaptive LASSO and the group of predictors extracted by the group LASSO were comparable to the coefficients obtained through ridge regression.
Regression model of water demand for the city of Lodz as a function of atmospheric factors
Authors: Domański Czesław , Kubacki Robert 
Published: October 2022

Abstract
One of the Sustainable Development Goals (Goal 6) set by the United Nations is to provide people with access to water and sanitation through sustainable water resources management. Water supply companies carrying out tasks commissioned by local authorities ensure there is an optimal amount of water in the water supply system. The aim of this study is to present the results of the work on a statistical model which determined the influence of individual atmospheric factors on the demand for water in the city of Lodz, Poland, in 2010-2019. In order to build the model, the study used data from the Water Supply and Sewage System Company (Zakład Wodociągów i Kanalizacji Sp. z o.o.) in the city of Lodz complemented with data on weather conditions in the studied period. The analysis showed that the constructed models make it possible to perform a forecast of water demand depending on the expected weather conditions.
Advances on Permutation Multivariate Analysis of Variance for big data
Authors: Bonnini Stefano , Assegie Getnet Melak 
Published: October 2022

Abstract
In many applications of the multivariate analyses of variance, the classic parametric solutions for testing hypotheses of equality in population means or multisample and multivariate location problems might not be suitable for various reasons. Multivariate multisample location problems lack a comparative study of the power behaviour of the most important combined permutation tests as the number of variables diverges. In particular, it is useful to know under which conditions each of the different tests is preferable in terms of power, how the power of each test increases when the number of variables under the alternative hypothesis diverges, and the power behaviour of each test as the function of the proportion of true alternative hypotheses. The purpose of this paper is to fill the gap in the literature about combined permutation tests, in particular for big data with a large number of variables. A Monte Carlo simulation study was carried out to investigate the power behaviour of the tests, and the application to a real case study was performed to show the utility of the method.
Scaled Fisher consistency for the partial likelihood estimation in various extensions of the Cox model
Authors: Bednarski Tadeusz , Nowak Piotr B. , Skolimowska-Kulig Magdalena 
Published: October 2022

Abstract
The Cox proportional hazards model has become the most widely used procedure in survival analysis. The theoretical basis of the original model has been developed in various extensions. In the recent years, vital research has been undertaken involving the incorporation of random effects to survival models. In this setting, the random effect is a variable (frailty) which embraces a variation among individuals or groups of individuals which cannot be explained by observable covariates. The right choice of the frailty distribution is essential for an accurate description of the dependence structure present in the data. In this paper, we aim to investigate the accuracy of inference based on the primer Cox model in the existence of unobserved heterogeneity, that is, when the data generating mechanism is more complex than presumed and described by the kind of an extension of the Cox model with undefined frailty. We show that the conventional partial likelihood estimator under the considered extension is Fisher-consistent up to a scaling factor, provided symmetry-type distributional assumptions on covariates. We also present the results of simulation experiments that reveal an exemplary behaviour of the estimators.
On the quick estimation of probability of recovery from COVID-19 during first wave of epidemic in India: a logistic regression approach
Authors: Joshi Hemlata , Azarudheen S. , Nagaraja M. S. , Chandraketu Singh 
Published: October 2022

Abstract
The COVID-19 pandemic has recently become a threat all across the globe with the rising cases every day and many countries experiencing its outbreak. According to the WHO, the virus is capable of spreading at an exponential rate across countries, and India is now one of the worst-affected country in the world. Researchers all around the world are racing to come up with a cure or treatment for COVID-19, and this is creating extreme pressure on the policy makers and epidemiologists. However, in India the recovery rate has been far better than in other countries, and is steadily improving. Still in such a difficult situation with no effective medicine, it is essential to know if a patient with the COVID-19 is going to recover or die. To meet this end, a model has been developed in this article to estimate the probability of a recovery of a patient based on the demographic characteristics. The study used data published by the Ministry of Health and Family Welfare of India for the empirical analysis.
Assessing the effect of new data sources on the consumer price index: a deterministic approach to uncertainty and sensitivity
Authors: Białek Jacek , Panek Tomasz , Zwierzchowski Jan 
Published: October 2022

Abstract
One of the greatest challenges facing official statistics in the 21st century is the use of alternative sources of data about prices (scanned and scraped data) in the analysis of price dynamics, which also involves selecting the appropriate formula of the price index at the elementary group (5-digit) level. When consumer price indices of goods and services are constructed, a number of subjective decisions are made at different stages, e.g. regarding the choice of data sources and types of indices used for the purpose of estimation. All of these decisions can affect the bias of consumer price indices, i.e. the extent to which they contribute to the overall uncertainty about the resulting index values. By measuring how robust consumer price indices are, one can assess the impact that the decisions made at the different stages of index construction have on the index values. This assessment involves analysing uncertainty and sensitivity. The purpose of the study described in the article was to determine how much and in which direction the consumer price index changes when including scanner and scraped data in the analysis, in addition to the data on prices collected by enumerators. The impact of these new data sources was assessed by analysing uncertainty and sensitivity under the deterministic approach. To the best of the authors’ knowledge, it is a novel application of robustness analysis to measure inflation using new data sources. The empirical study was based on data for February and March 2021, while scanner and scraped data about selected categories of food products were obtained from one retail chain operating hundreds of points of sale in Poland and selling products online. It was found that the choice of a data source has the most significant impact on the final value of the index at the elementary group level, while the choice of the aggregation formula used to consolidate different data sources is of secondary importance.
A comparison of the method of moments estimator and maximum likelihood estimator for the success probability in the Fibonacci-type probability distribution
Authors: Kwon Yeil 
Published: October 2022

AbstractA Fibonacci-type probability distribution provides the probabilistic models for establishing stopping rules associated with the number of consecutive successes. It can be interpreted as a generalized version of a geometric distribution. In this article, after revisiting the Fibonacci-type probability distribution to explore its definition, moments and properties, we proposed numerical methods to obtain two estimators of the success probability: the method of moments estimator (MME) and maximum likelihood estimator (MLE). The ways both of them performed were compared in terms of the mean squared error. A numerical study demon-srated that the MLE tends to outperform the MME for most of the parameter space with various sample sizes.
Interval shrinkage estimation of the parameter of exponential distribution in the presence of outliers under loss functions
Authors: Nasiri Parviz 
Published: October 2022

Abstract
In this paper, we studied estimators based on an interval shrinkage with equal weights point shrinkage estimators for all individual target points ¯θ ∈ (θ
0,θ
1) for exponentially distributed observations in the presence of outliers drawn from a uniform distribution. Estimators obtained from both shrinkage and interval shrinkage were compared, showing that the estimators obtained via the interval shrinkage method perform better. Symmetric and asymmetric loss functions were also used to calculate the estimators. Finally, a numerical study and illustrative examples were provided to describe the results.
An improved ridge type estimator for logistic regression
Authors: Varathan Nagarajah 
Published: October 2022

Abstract
In this paper, an improved ridge type estimator is introduced to overcome the effect of multi-collinearity in logistic regression. The proposed estimator is called a modified almost unbiased ridge logistic estimator. It is obtained by combining the ridge estimator and the almost unbiased ridge estimator. In order to asses the superiority of the proposed estimator over the existing estimators, theoretical comparisons based on the mean square error and the scalar mean square error criterion are presented. A Monte Carlo simulation study is carried out to compare the performance of the proposed estimator with the existing ones. Finally, a real data example is provided to support the findings.
Impact of restrictions on the COVID-19 pandemic situation in Poland
Authors: Herman Sergiusz 
Published: October 2022

Abstract
The COVID-19 pandemic has had a substantial impact on public health all over the world. In order to prevent the spread of the virus, the majority of countries introduced restrictions which entailed considerable economic and social costs. The main goal of the article is to study how the lockdown introduced in Poland affected the spread of the pandemic in the country. The study used synthetic control method to this end. The analysis was carried on the basis of data from the Local Data Bank and a government website on the state of the epidemic in Poland.
The results indicated that the lockdown significantly curbed the spread of the COVID-19 pandemic in Poland. Restrictions led to the substantial drop in infections – by 9500 cases – in three weeks. The results seem to stay the same despite the change of assumptions in the study. Such conclusion can be drawn from the performance of the placebo-in-space and placebo-in-time analyses.
PENGARUH GOOD CORPORATE GOVERNANCE, FINANCIAL DISTRESS, DAN LIKUIDITAS TERHADAP KUALITAS LABA
Authors: Tita Aprilianus Wanrizky , Pohan Hotman Tohir 
Published: October 2022

This research was conducted to analyze the effect of good corporate governance, financial distress, and liquidity on earnings quality. The data usedin this study is secondary data obtained from financial statements manufacturing companies listedon the Indonesia Stock Exchange (IDX) for the 2016-2020 period. In this study, earnings quality is measured by discretionary accruals using the Modified Jones Model. From the data collection process, obtained final sample used will be in research asmuch as 305 sample. The research model used in this study is analysis regression multiple linear. The results of this study indicate that the variable proportion of the Independent Board of Commissioners, of the Audit Committee, financial distress, and liquidity take effect on earnings quality.
Keywords: The proportion of independent commissioners, of the audit committee, financial distress, liquidity, earnings quality
Acute subdural hematoma associated with disruption of tumor vessels due to rapid growth of falx meningioma: A case report and literature review
Authors: Oyamada Kohei , Ohbuchi Hidenori , Nishiyama Kae , Imazato Daisuke , Inazuka Mayuko , Hagiwara Shinji , Kubota Yuichi 
Published: October 2022

Background:
Meningiomas associated with acute subdural hematoma (ASDH) are rare. The rapid growth of meningiomas has been shown to be one of the mechanisms underlying bleeding. We report the first case of ASDH during an imaging follow-up for the rapid growth of a falx meningioma.

Case Description:
A 77-year-old woman was diagnosed with an incidental tumor along the right falx cerebri 3 years before bleeding. The follow-up magnetic resonance imaging (MRI) after 3 years showed that the tumor volume had rapidly increased from 4.31 cm3 to 22.27 cm3. The blood vessels around the tumor were stretched. The patient was scheduled to undergo tumor removal surgery. However, the patient experienced a sudden onset of disturbance of consciousness and was transferred to our hospital. On arrival, her Glasgow Coma Scale (GCS) score was 6 (E1V1M4) and right hemiplegia was observed. The patient had no history of traumatic events. Computed tomography (CT) showed left hemispheric and interhemispheric ASDH. Digital subtraction angiography revealed neither tumor staining nor abnormal vessels. Gross total tumor removal and hematoma evacuation were performed. There were no obvious active intraoperative bleeding points. The pathologic diagnosis was meningioma, the World Health Organization Grade I. Postoperative course revealed a GCS score of 10 (E4V1M5) and she was transferred to a rehabilitation hospital.

Conclusion:
The disruption of tumor vessels due to the rapid growth of meningiomas may be a cause of bleeding. Incidental falx meningiomas with stretched tumor vessels due to rapid growth could indicate the need for early surgery.