SUB-SAMPLE MANN–WHITNEY–WILCOXON TEST

ABSTRACT

This paper presents a class of sub-sample rank-sum statistics to test the stochastic ordering between two distributions. The proposed class includes as special case the min–max test of Öztürk.[1] Öztürk, Ö. 2001. A Generalization of Ahmad's Class of Mann–Whitney–Wilcoxon Statistics. Austrl. and New Zealand J. Statist., 43: 67–74.  [Google Scholar] It is shown that the asymptotic distribution of the test statistic is normal and its Pitman asymptotic efficiency is as good as or higher than the competitors in the class of sub-sample Mann–Whitney–Wilcoxon statistics.     

Inverse Circular–Linear/Linear–Circular Regression

We propose two distance-based methods and two likelihood-based methods of inversely regressing a linear predictor on a circular variable, and of inversely regressing a circular predictor on a linear variable. An asymptotic result on least circular distance estimators is provided in the Appendix. We present likelihood-based methods for symmetrical and asymmetrical errors in each situation. The utility of our methodology in each situation is illustrated by applying it to real data sets in engineering and environmental science. We then compare their performances using a cross validation method.     

Marshall–Olkin gamma–Weibull distribution with applications

Abstract

A Marshall–Olkin variant of the Provost type gamma–Weibull probability distribution is being introduced in this paper. Some of its statistical functions and numerical characteristics among others characteristics function, moment generalizing function, central moments of real order are derived in the computational series expansion form and various illustrative special cases are discussed. This density function is utilized to model two real data sets. The new distribution provides a better fit than related distributions as measured by the Anderson–Darling and Cramér–von Mises statistics. The proposed distribution could find applications for instance in the physical and biological sciences, hydrology, medicine, meteorology, engineering, etc.     

Additional Tables for Steel–Dwass–Critchlow–Fligner Distribution-Free Multiple Comparisons of Three Treatments

ABSTRACT

Additional critical points are presented for the Steel–Dwass–Critchlow–Fligner distribution-free multiple comparison procedure for comparing all pairs of three population medians in the one-way layout. A computational technique developed by van de Wiel is used to find critical points yielding an experimentwise error rate of approximately 0.01, 0.05, and 0.10 for a total sample size of at most 30, with individual sample sizes from 4 to 10 and a maximum sample size of at least 8, and for equal sample sizes from 8 to 14. Additional discussion is given regarding step-down testing methods and the dangers of using the Steel–Dwass–Critchlow–Fligner procedure with unequal sample sizes if two of the sample sizes are very small.     

Approximating the Conway–Maxwell–Poisson distribution normalization constant

By adding a second parameter, Conway and Maxwell created a new distribution for situations where data deviate from the standard Poisson distribution. This new distribution contains a normalization constant expressed as an infinite sum whose summation has no known closed-form expression. Shmueli et al. produced an approximation for this sum but proved that it was valid only for integer values of the second parameter, although they conjectured it was also valid for non-integers. Here we prove their conjecture to be true and discuss for what range of parameters the approximation can be accurately applied.     

Marshall–Olkin q-Weibull distribution and max–min processes

In this paper, we introduce a new probability model known as Marshall–Olkin q-Weibull distribution. Various properties of the distribution and hazard rate functions are considered. The distribution is applied to model a biostatistical data. The corresponding time series models are developed to illustrate its application in times series modeling. We also develop different types of autoregressive processes with minification structure and max–min structure which can be applied to a rich variety of contexts in real life. Sample path properties are examined and generalization to higher orders are also made. The model is applied to a time series data on daily discharge of Neyyar river in Kerala, India.     

Bias–variance and breadth–depth tradeoffs in respondent-driven sampling

Respondent-driven sampling (RDS) is a link-tracing network sampling strategy for collecting data from hard-to-reach populations, such as injection drug users or individuals at high risk of being infected with HIV. The mechanism is to find initial participants (seeds), and give each of them a fixed number of coupons allowing them to recruit people they know from the population of interest, with a mutual financial incentive. The new participants are again given coupons and the process repeats. Currently, the standard RDS estimator used in practice is known as the Volz–Heckathorn (VH) estimator. It relies on strong assumptions about the underlying social network and the RDS process. Via simulation, we study the relative performance of the plain mean and VH estimators when assumptions of the latter are not satisfied, under different network types (including homophily and rich-get-richer networks), participant referral patterns, and varying number of coupons. The analysis demonstrates that the plain mean outperforms the VH estimator in many but not all of the simulated settings, including homophily networks. Also, we highlight the implications of multiple recruitment and varying referral patterns on the depth of RDS process. We develop interactive visualizations of the findings and RDS process to further build insight into the various factors contributing to the performance of current RDS estimation techniques.     

Smoothed Mann–Whitney–Wilcoxon Procedure for Two-Sample Location Problem

This study is mainly concerned with estimating a shift parameter in the two-sample location problem. The proposed Smoothed Mann–Whitney–Wilcoxon method smooths the empirical distribution functions of each sample by using convolution technique, and it replaces unknown distribution functions F(x) and G(x ? Δ₀) with the new smoothed distribution functions F _s (x) and G _s (x ? Δ₀), respectively. The unknown shift parameter Δ₀ is estimated by solving the gradient function S _n (Δ) with respect to an arbitrary variable Δ. The asymptotic properties of the new estimator are established under some conditions that are similar to the Generalized Wilcoxon procedure proposed by Anderson and Hettmansperger (1996 Anderson , G. F. , Hettmansperger , T. P. ( 1996 ). Generalized Wilcoxon methods for the one and two-sample location models . In: Brunner , E. , Denker , M. , eds. Research Developments in Probability and Statistics: Festschrift in Honor of Madan L. Puri on the Occasion of his 65th Birthday . Zeist, The Netherlands : VSP BV , pp. 303 – 317 . [Google Scholar]). Some of these properties are asymptotic normality, asymptotic level confidence interval, and hypothesis testing for Δ₀. Asymptotic relative efficiency of the proposed method with respect to the least squares, Generalized Wilcoxon and Hodges and Lehmann (1963 Hodges , J. L. , Lehmann , E. L. ( 1963 ). Estimates of location based on rank tests . Ann. Mathemat. Statist. 34 : 598 – 611 .[Crossref] , [Google Scholar]) procedures are also calculated under the contaminated normal model.     

Reliability characteristics of Farlie–Gumbel–Morgenstern family of bivariate distributions

Abstract

In this paper, we study the Farlie–Gumbel–Morgenstern family of bivariate distributions from a reliability point of view. The properties of this family of distributions and the association between the two variables are investigated by studying the local dependence function and the association measure defined by Clayton (1978 Clayton, D.G. (1978). A model for association in bivariate life tables and its applications in epidemiological studies of familial tendency in chronic disease incidence. Biometrika 65:141–151.[Crossref], [Web of Science ®] , [Google Scholar]). We also study the effect of the association parameter on the hazard components, the failure rate of the series system, and the regression mean residual life of a parallel system. Stochastic comparisons with respect to the association parameter are also studied. Some examples are provided to illustrate the results.     

On Nelson–Aalen type estimation in the partial Koziol–Green model

In this paper, a Nelson–Aalen (NA) type estimator is derived and its sample properties are compared with the partial Abdushukurov–Cheng–Lin (PACL), generalized maximum likelihood (GMLE), and Kaplan–Meier (KM) estimators under the partial Koziol–Green model. These comparisons are made through Monto Carlo simulations under various sample sizes. The results indicate that the NA estimator always performs better than the KM estimator and is competitive with other estimators. Moreover, the PACL, GMLE, and NA estimators are shown to be asymptotically equivalent.     

Generalized Poisson–Lindley Distribution

An extended version of the compound Poisson distribution is obtained by compounding the Poisson distribution with the generalized Lindley distribution. Estimation of the parameters is discussed using the method of moments and maximum likelihood estimators. Examples are given of the fitting of this distribution to data, and the fit is compared with that obtained using other distributions.     

Covariate-adjusted cost–effectiveness ratios

We describe a method for estimating the marginal cost–effectiveness ratio (CER) of two competing treatments or intervention strategies after adjusting for covariates that may influence the primary endpoint of survival. A Cox regression model is used for modeling covariates and estimates of both the cost and effectiveness parameters, which depend on the survival curve, are obtained from the estimated survival functions for each treatment at a specified covariate. Confidence intervals for the covariate-adjusted CER are presented.     

Age–period–cohort decompositions using principal components and partial least squares

Age–period–cohort decomposition requires an identification assumption because there is a linear relationship between age, survey period, and birth cohort (age+cohort=period). This paper proposes new decomposition methods based on factor models such as principal components model and partial least squares model. Although factor models have been applied to overcome the problem of many observed variables with possible co-linearity, they are applied to overcome the perfect co-linearity among age, period, and cohort dummy variables. Since any unobserved factor in the factor model is represented as a linear combination of the observed variables, the parameter estimates for age, period, and cohort effects are automatically obtained after the application of these factor models. Simulation results suggest that in almost all cases, the performance of the proposed method is better than that of a conventional econometric method. Empirical examples are also provided.     

Durbin–Watson and Generalized Durbin–Watson Tests for Autocorrelations and Randomness

The Durbin–Watson (DW) test for lag 1 autocorrelation has been generalized (DWG) to test for autocorrelations at higher lags. This includes the Wallis test for lag 4 autocorrelation. These tests are also applicable to test for the important hypothesis of randomness. It is found that for small sample sizes a normal distribution or a scaled beta distribution by matching the first two moments approximates well the null distribution of the DW and DWG statistics. The approximations seem to be adequate even when the samples are from nonnormal distributions. These approximations require the first two moments of these statistics. The expressions of these moments are derived.     

A Bayesian analysis of the Conway–Maxwell–Poisson cure rate model

The purpose of this paper is to develop a Bayesian analysis for the right-censored survival data when immune or cured individuals may be present in the population from which the data is taken. In our approach the number of competing causes of the event of interest follows the Conway–Maxwell–Poisson distribution which generalizes the Poisson distribution. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the proposed model. Also, some discussions on the model selection and an illustration with a real data set are considered.     

A note on Jarque–Bera normality test for ARMA–GARCH innovations

In this paper, we study the Jarque–Bera (JB) normality test for the innovations of ARMA–GARCH models, whose construction is based on the residuals. The validity of the JB test for ARMA–GARCH innovations should be carefully investigated in advance of actual practice, since the residual-based test may behave differently, depending upon the structure of the time series models and the form of the test statistic (cf. Chen and Kuan, 2003, Hwang and Baek, 2009, Lee and Wei, 1999). In order to demonstrate the validity of the JB test, we prove that the asymptotic distribution of the original form of the JB test is identical to that of the test statistic based on true errors under mild conditions. Simulation results are provided for illustration.     

Testing for co-directional interactions using union–intersection and intersection–union methods

When interaction terms exist in a two-factor, factorial experiment, the consideration and analysis of main effects are often restricted to those situations where the interaction between factors is not significant. Hinkelman and Kempthorne [4 Hinkelmann, K. and Kempthorne, O. 1994. Design and Analysis of Experiments. Volume 1: Introduction to Experimental Design, New York: Wiley.  [Google Scholar]] softened that stance somewhat and advocate testing main effects when the interaction is deemed co-directional but not anti-directional. A test for the main effects in that situation may be pragmatic to the practitioner and appealing to researchers in other disciplines. Intersection–union and union–intersection methods are examined for assessing the directional nature of significant interactions so that the main effects in a two-factor factorial may be evaluated. The tests suggested are conceptually straightforward and practical and maintain the nominal Type-I error rate. Examples are provided to illustrate the methods.