Zahid Asghar

Transmuted Power Function Distribution : A more flexible Distribution

Abstract Double-bounded data appears in many applications related to engineering, economics, hydrology, social and behavioral sciences. To model the double-bounded data the Power function distribution is an obvious choice. To enhance its applicability, we proposed transmuted Power function distribution in this study. The new distribution relatively more flexible and performs better in reliability and meteorological data analysis than the parent distribution. Its various mathematical properties are derived such as mean, mode, median, variance, quantile function, reliability function and hazard function. The order statistics and generalized TL-moments with its special cases L-, TL-, LL- and LH-moments are also explored. Parameters estimation is approached through method of maximum likelihood estimation and to evaluate their performance a simulation study has been carried out. Finally, the transmuted and parent distributions is illustrated and compared through two real data sets.

Performance Evaluation of Different Tests for Location Parameters

In this article, our objective is to evaluate the performance of different tests which are used to compare the equality of more than two location parameters. We have considered six tests (including some commonly used) in this study, one of which is parametric and the others are nonparametric. These tests include the usual F test (Fisher and Mackenzie, 1923), Kruskal–Wallis test (Kruskall and Wallis, 1952), Kolmogorov–Smirnov test (David, 1958), the g test (Stekler, 1987), f test (Batchelor, 1990), and Extension of Median test (as given in Daniel, 1990). Performance of these tests are compared under different symmetric, skewed and contaminated probability distributions that include Normal, Cauchy, Uniform, Laplace, Lognormal, Exponential, Weibull, Gamma, t, Chi-square, Half Normal, Mixed Weibull, and Mixed Normal. Performances of these tests are measured in terms of power. We have suggested appropriate tests which may perform better under different situations. It is expected that researchers will find these results useful in decision making.

Analysis of the performance of test statistics for detection of outliers (additive, innovative, transient, and level shift) in AR (1) processes

ABSTRACT Outlier detection has always been of interest for researchers and data miners. It has been well researched in different knowledge and application domains. This study aims at exploring the correctly identifying outliers using most commonly applied statistics. We evaluate the performance of AO, IO, LS, and TC as vulnerability to spurious outliers by means of empirical level of significance (ELS), power of the test indicating the sensitivity of the statistical tests in detecting changes and the vulnerability to masking of outliers in terms of misspecification frequencies are determined. We have observed that the sampling distribution of test statistic ηtp; tp = AO, IO, LS, TC  in case of AR(1) model is connected with the values of n and φ. The sampling distribution of  ηTC  is less concentrated than the sampling distribution of ηAO, ηIO, and ηLS. In AR(1) process, empirical critical values for 1%, 5%, and 10% upper percentiles are found to be higher than those generally used. We have also found the evidence that the test statistics for transient change (TC) needs to be revisited as the test statistics ηTC is found to be eclipsed by ηAO, ηLS and ηIO at different δ values. TC keeps on confusing with IO and AO, and at extreme δ values it just gets equal to AO and LS.

On evaluating the performance of different forecasters

ABSTRACT In this article, we have evaluated the performance of different forecasters and tested association between their performances for different pairs of variables. We have used three data sets of track records of professional U.S. economic forecasters participating in the Blue Chip consensus forecasting service (the data sets contain the root mean square errors (RMSE) of different forecasters for different years). To evaluate the performance of forecasters we have covered three well-known tests, namely the usual F test (cf. Fisher (1923)), Kruskal Wallis test (cf. Kruskal and Wallis (1952)), and Extension of Median test (cf. Daniel (1990)). To test the association between the forecasters performances for different pairs of variables, we have considered Gini mean correlation coefficient rg1 (cf. Yitzhaki, S., and Olkin, I. (1991) and Yitzhaki (2003)), Modified rank correlation coefficient (cf. Zimmerman (1994)) and three modifications of Spearman rank correlation coefficient. We have observed that different forecasters do not necessarily offer same average performance. Moreover, an evidence of association between two criteria does not always lead us reaching at the same decision. The outcomes of the study may help the practitioners in selecting the best forecaster(s) for policymaking purposes.

Analysis of seasonal level shift (SLS) detection in SARIMA models

ABSTRACT This study aims at exploring correct identification of seasonal outliers using most commonly applied test statistics. We evaluate the performance of seasonal level shift (SLS) by means of empirical level of significance, power of the test for sensitivity in detecting changes, and the vulnerability to masking of outliers by misspecification frequencies. We observe that the size of SLS affects the sampling distribution of ηSLS (test statistics for SLS detection) in case of SAR (1) and SMA (1) model. The empirical critical values for 1%, 5%, and 10% upper percentiles are higher than the usual cut off points and the empirical level of significance is inversely related to sample size and the model coefficients. The empirical power of the test statistics is not satisfactory at small sample size, and for large model coefficient. ηSLS gets confused with IO. The potential list of types of outliers should retain both IO and SLS as a part of outlier detection procedure for most efficient results. We apply the method suggested by Kaiser and Maravall with five possible types of outliers, that is, AO, IO, LS, TC, and SLS, to a number of quarterly and monthly time series data from Pakistan.