A Unified Approach to Estimating and Testing Income Distributions With Grouped Data

We propose a unified approach that is flexibly applicable to various types of grouped data for estimating and testing parametric income distributions. To simplify the use of our approach, we also provide a parametric bootstrap method and show its asymptotic validity. We also compare this approach with existing methods for grouped income data, and assess their finite-sample performance by a Monte Carlo simulation. For empirical demonstrations, we apply our approach to recovering China's income/consumption distributions from a sequence of income/consumption share tables and the U.S. income distributions from a combination of income shares and sample quantiles. Supplementary materials for this article are available online.     

Psychometric testing of the Chinese version of ISPCAN Child Abuse Screening Tools Children's Home Version (ICAST-CH-C)

BackgroundChild maltreatment is a global problem and the true extent remains unknown. The International Society for the Prevention of Child Abuse and Neglect (ISPCAN) Child Abuse Screening Tool — Children's Home version (ICAST-CH) has provided accurate assessment of the scope and prevalence of child maltreatment. Yet measures of children's experiences of child maltreatment are limited in the Chinese population.ObjectivesThe study aimed to translate and validate a Chinese version of the ISPCAN Child Abuse Screening Tool — Children's Home version (ICAST-CH) and to evaluate its reliability and validity among Taiwan adolescents.MethodsA three phase study was conducted. In phase 1, the ICAST was translated into Chinese using forward–backward translation procedures with the translation equivalence and content validity assessed. In phase 2, the data provided by a convenience sample of 98 adolescents was used to assess the internal consistency of the ICAST-CH Chinese version (ICAST-CH-C). In phase 3, the psychometric properties of the ICAST-CH-C were tested with a nationwide random sample of 5236 adolescents from 35 schools.ResultsThe translation equivalence and content validity index of the ICAST-CH-C was satisfactory. The inter-rater agreements were .90–.91 for comparability of language and .89–.94 for similarity of interpretability. Results indicated that the ICAST-CH-C had a high level of equivalence with the original English version and demonstrated a high internal consistency (.71–.89). Confirmatory factor analysis revealed the presence of five factors supporting the conceptual dimension of the original instrument.ConclusionThis study provided initial psychometric properties of the ICAST-CH-C and supports it as a reliable, valid, and highly usable instrument to identify childhood victimization in adolescents. It provided health care professionals with a useful tool to assess the severity and prevalence of child maltreatment within Chinese communities.     

Joint modelling of longitudinal binary data and survival data

The medical costs in an ageing society substantially increase when the incidences of chronic diseases, disabilities and inability to live independently are high. Healthy lifestyles not only affect elderly individuals but also influence the entire community. When assessing treatment efficacy, survival and quality of life should be considered simultaneously. This paper proposes the joint likelihood approach for modelling survival and longitudinal binary covariates simultaneously. Because some unobservable information is present in the model, the Monte Carlo EM algorithm and Metropolis-Hastings algorithm are used to find the estimators. Monte Carlo simulations are performed to evaluate the performance of the proposed model based on the accuracy and precision of the estimates. Real data are used to demonstrate the feasibility of the proposed model.     

Evaluations of FDR-controlling procedures in multiple hypothesis testing

Many exploratory experiments such as DNA microarray or brain imaging require simultaneously comparisons of hundreds or thousands of hypotheses. Under such a setting, using the false discovery rate (FDR) as an overall Type I error is recommended (Benjamini and Hochberg in J. R. Stat. Soc. B 57:289–300, 1995). Many FDR controlling procedures have been proposed. However, when evaluating the performance of FDR-controlling procedures, researchers are often focused on the ability of procedures to control the FDR and to achieve high power. Meanwhile, under the multiple hypotheses, it may be also likely to commit a false non-discovery or fail to claim a true non-significance. In addition, various experimental parameters such as the number of hypotheses, the proportion of the number of true null hypotheses to the number of hypotheses, the samples size and the correlation structure may affect the performance of FDR controlling procedures. The purpose of this paper is to illustrate the performance of some existing FDR controlling procedures in terms of four indices, i.e., the FDR, the false non-discovery rate, the sensitivity and the specificity. Analytical results of these indices for the FDR controlling procedures are derived. Simulations are also performed to evaluate the performance of controlling procedures in terms of these indices under various experimental parameters. The result can be used to summarize as a guidance for practitioners to properly choose a FDR controlling procedure.     

A new functional statistic for multivariate normality

The test statistics of assessing multivariate normality based on Roy’s union-intersection principle (Roy, Some Aspects of Multivariate Analysis, Wiley, New York, 1953) are generalizations of univariate normality, and are formed as the optimal value of a nonlinear multivariate function. Due to the difficulty of solving multivariate optimization problems, researchers have proposed various approximations. However, this paper shows that the (nearly) global solution contrarily results in unsatisfactory power performance in Monte Carlo simulations. Thus, instead of searching for a true optimal solution, this study proposes a functional statistic constructed by the q% quantile of the objective function values. A comparative Monte Carlo analysis shows that the proposed method is superior to two highly recommended tests when detecting widely-selected alternatives that characterize the various properties of multivariate normality.     

Testing Hysteresis in Unemployment in G7 Countries Using Quantile Unit Root Test with both Sharp Shifts and Smooth Breaks

Social Indicators Research - We apply a Quantile unit root test with both Sharp Shifts and Smooth Breaks to revisit hysteresis in unemployment for G7 countries using data for the period...     

Comparisons of normalization methods for relative quantitative in real-time polymerase chain reaction

The real-time polymerase chain reaction (rtPCR) provides sensitive and accurate quantitative results and becomes a widespread technique in analyzing gene expressions. House-keeping genes are required as references to normalize data of target genes, which may be unstable. This normalization process is similar to the normalization in analyzing high-density oligonucleotide arrays. This article evaluates the feasibility of normalizations for high-density oligonucleotide arrays to normalize data collected in rtPCR experiments. Since data features are different, simulations are used to evaluate the performance of these normalizations to rtPCR data based on five indices. Their feasibilities are illustrated by a rtPCR data.     

A Remark on the Zhang Omnibus Test for Normality

Zhang (1999) proposed a novel test statistic Q for testing normality based on the ratio of two unbiased standard deviation estimators, q₁ and q₂, for the true population standard deviation σ. Mingoti & Neves (2003) discussed some properties of q₁ and q₂ and showed that the variance of q₁ increases as the true population variance increases. In this paper, we show that the distribution of q₁ is not normal. As a result, normality percentage points for Q are not appropriate. In this paper, percentage points of Q are obtained using simulations. Monte Carlo simulations are provided to evaluate the performance of the new method and Zhang's method.     

Estimating the number of true null hypotheses in multiple hypothesis testing

The overall Type I error computed based on the traditional means may be inflated if many hypotheses are compared simultaneously. The family-wise error rate (FWER) and false discovery rate (FDR) are some of commonly used error rates to measure Type I error under the multiple hypothesis setting. Many controlling FWER and FDR procedures have been proposed and have the ability to control the desired FWER/FDR under certain scenarios. Nevertheless, these controlling procedures become too conservative when only some hypotheses are from the null. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) proposed an adaptive FDR-controlling procedure that adapts the information of the number of true null hypotheses (m ₀) to overcome this problem. Since m ₀ is unknown, estimators of m ₀ are needed. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) suggested a graphical approach to construct an estimator of m ₀, which is shown to overestimate m ₀ (see Hwang in J. Stat. Comput. Simul. 81:207–220, 2011). Following a similar construction, this paper proposes new estimators of m ₀. Monte Carlo simulations are used to evaluate accuracy and precision of new estimators and the feasibility of these new adaptive procedures is evaluated under various simulation settings.     

Modeling exotic highly pathogenic avian influenza virus entrance risk through air passenger violations

The highly pathogenic avian influenza virus (HPAIV) is able to survive in poultry products and could be carried into a country by air travelers. An assessment model was constructed to estimate the probability of the exotic viable HPAIV entering Taiwan from two neighboring areas through poultry products carried illegally by air passengers at Taiwan's main airports. The entrance risk was evaluated based on HPAIV-related factors (the prevalence and the incubation period of HPAIV; the manufacturing process of poultry products; and the distribution-storage-transportation factor event) and the passenger event. Distribution functions were adopted to simulate the probabilities of each HPAIV factor. The odds of passengers being intercepted with illegal poultry products were estimated by logistic regression. The Monte Carlo simulation established that the risk caused by HPAIV-related factors from area A was lower than area B, whereas the entrance risk by the passenger event from area A was similar to area B. Sensitivity analysis showed that the incubation period of HPAIV and the interception of passenger violations were major determinants. Although the result showed viable HPAIV was unlikely to enter Taiwan through meat illegally carried by air passengers, this low probability could be caused by incomplete animal disease data and modeling uncertainties. Considering the negative socioeconomic impacts of HPAIV outbreaks, strengthening airport quarantine measures is still necessary. This assessment provides a profile of HPAIV entrance risk through air travelers arriving from endemic areas and a feasible direction for quarantine and public health measures.