New two-stage sampling designs based on neoteric ranked set sampling

Neoteric ranked set sampling (NRSS) is a recently developed sampling plan, derived from the well-known ranked set sampling (RSS) scheme. It has already been proved that NRSS provides more efficient estimators for population mean and variance compared to RSS and other sampling designs based on ranked sets. In this work, we propose and evaluate the performance of some two-stage sampling designs based on NRSS. Five different sampling schemes are proposed. Through an extensive Monte Carlo simulation study, we verified that all proposed sampling designs outperform RSS, NRSS, and the original double RSS design, producing estimators for the population mean with a lower mean square error. Furthermore, as with NRSS, two-stage NRSS estimators present some bias for asymmetric distributions. We complement the study with a discussion on the relative performance of the proposed estimators. Moreover, an additional simulation based on data of the diameter and height of pine trees is presented.     

Estimating the population proportion in modified ranked set sampling methods

In this paper, proportion estimators and associated variance estimators are proposed for a binary variable with a concomitant variable based on modified ranked set sampling methods, which are extreme ranked set sampling (ERSS), median ranked set sampling (MRSS), percentile ranked set sampling (Per-RSS) and L ranked set sampling (LRSS) methods. The Monte Carlo simulation study is performed to compare the performance of the estimators based on bias, mean squared error, and relative efficiency for different levels of correlation coefficient, set and cycle sizes under normal and log-normal distributions. Moreover, the study is supported with real data application.     

A new financial stress index model based on support vector regression and control chart

Financial stress index (FSI) is considered to be an important risk management tool to quantify financial vulnerabilities. This paper proposes a new framework based on a hybrid classifier model that integrates rough set theory (RST), FSI, support vector regression (SVR) and a control chart to identify stressed periods. First, the RST method is applied to select variables. The outputs are used as input data for FSI–SVR computation. Empirical analysis is conducted based on monthly FSI of the Federal Reserve Bank of Saint Louis from January 1992 to June 2011. A comparison study is performed between FSI based on the principal component analysis and FSI–SVR. A control chart based on FSI–SVR and extreme value theory is proposed to identify the extremely stressed periods. Our approach identified different stressed periods including internet bubble, subprime crisis and actual financial stress episodes, along with the calmest periods, agreeing with those given by Federal Reserve System reports.     

The matched pair sign test using bivariate ranked set sampling for different ranking based schemes

Bivariate rank set sample (BVRSS) matched pair sign test is introduced and investigated for different ranking based schemes. We show that this test is asymptotically more efficient and more powerful than its counterpart sign test based on a bivariate simple random sample (BVSRS) for different ranking schemes. The asymptotic null distribution and the efficiency of the test are derived. Pitman’s asymptotic relative efficiency is used to compare the asymptotic performance of the matched pair sign test using BVRSS versus using BVSRS in all ranking cases. For small sample sizes, the bootstrap method is used to estimate P-values. Numerical comparisons are used to gain insight about the efficiency of the BVRSS sign test compared to the BVSRS sign test. Our numerical and theoretical results indicate that using any ranking scheme of BVRSS for the matched pair sign test is more efficient than using BVSRS.     

Inference in the presence of ranking error in ranked set sampling

The author proposes inference techniques for ranked set sample data in the presence of judgment ranking errors. He bases his analysis on the models of Bohn & Wolfe (1994) and Frey (2007a, b), of which parameters are estimated by minimizing a distance measure. He then uses the fitted models to calibrate confidence intervals and tests. He shows the validity of his approach through simulation and illustrates its application through the construction of distribution‐free confidence intervals for the median area of apple tree leaves covered by a spray.     

一种基于DEA模型的粗集决策方法

将DEA方法引入粗集理论中,提出一种可解决“具有多个决策属性特征”的多投入、多产出问题的方法,建立一种基于DEA的粗集决策模型,并通过研究中国生产力水平的效率问题来验证模型的合理性和有效性,为人们进行科学的决策提供了一种新的思想和方法。     

Diverging Paths? A Comparative Look at Childcare Policies in Japan,South Korea and Taiwan

This article compares state policies to support childcare in Japan, South Korea and Taiwan, using fuzzy set ideal type analysis to determine the nature of institutional arrangements with respect to labour, money and time provisions. We then note their implications for familialization and defamilialization in the three countries. Our analysis suggests a common pattern towards the increased use of financial support amongst the three countries over time; however, this commonality does not mean their childcare policies are converging, as the financial supports differ in focus, with Japan concentrating on familialization by valuing family care, and Korea exclusively employing policy to facilitate the use of market‐based care services. For its part, Taiwan has been strengthening familialization by increasing the leave compensation to value time off to provide care. The different labour, money and time dimensions vis‐à‐vis the familialization/defamilialization matrix suggest varying implications of institutional arrangements for gender.     

The backfiring effect of auditing on tax compliance

Using country-level data from 2003–2014, we examine the association between auditing level (measured as number of verification actions taken by tax authorities per 100 taxpayers in each country) and tax compliance (measured as business executives’ perception of tax evasion). Our hypothesis is that compliance increases until a certain auditing level is reached, and decreases beyond that level (i.e., an elevated auditing level backfires). In line with our expectation, the results of a series of tests indicate that there is a U-shaped association between auditing and tax evasion. We discuss how a potential backfiring effect may depend on the extent to which compliance is voluntary.