by Michele Zenga
A new three-parameter density function f ðx : ; ; Þ for non-negative variables, obtained as a mixture of ‘‘Polisicchio’s truncated Pareto distributions’’, is proposed. The expectation of f ðx : ; ; Þ
is equal to the parameter > 0. The new density has positive asymmetry and Paretian right tail. The variance is equal to 2 3
ð þ 1Þ
½2 þ ð 1Þ
. The moments, the upper and lower truncated moments
(with truncation at x ¼ ), are compact expressions of beta functions.
Keywords: Income Distribution, Positive Asymmetry, Paretian Tail, Mixture Density, Beta Weights
by Jules J. de Tibeiro, Luigi D'Ambra
Problems involving dependent pairs of random variables usually involve two aspects: tests of independence or estimation of measures of association. In order to find out which way best explains the data, this paper addresses Regression Analysis applied to Correspondence Analysis (CA). It also uses Agglomerative Hierarchical Clustering as a method to accompany Multiple Correspondence Analysis (MCA). A well known data set is analyzed.
Keywords: Complete Disjunctive Table, Burt Matrix, Regression Table, Multiple Correspondence Analysis, Agglomerative Hierarchical Clustering.
by Luca Bagnato, Antonio Punzo
In this paper two tests of serial independence are proposed. The building block of these procedures is the definition of a component 2-test for testing independence between pairs of lagged variables. With reference to different component 2-tests, it is shown that the corresponding test statistics are independent. Taking advantage of this result, the component tests are used from both a simultaneous and a direct viewpoint to define two different test procedures denoted by SIS (Serial Independence Simultaneous) and SICS (Serial Independence Chi-Square). Simulations are used to explore the performance of these tests in terms of size and power. Our results underline that both the proposals are powerful against various types of alternatives. It is also shown, through what we call Lag Subsets Dependence Plot (LSDP), how to detect possible lag(s)-dependences graphically. Some examples are finally provided in order to evaluate the effectiveness of the LSDP.
Keywords: Nonlinear Time Series, Serial Independence, Simultaneous Tests, 2-test.
by Z.R. Al-Rawi, Mohd T. Alodat, Walid Ahmad Abu-Dayyeh
In this paper, we consider the problem of constructing confidence intervals for the normal population mean when the coefficient of variation is known. We obtain 13 confidence intervals using different pivots. Also we conduct a simulation study to compare the expectations as well as the standard deviations of the intervals lengths.
Keywords: Coefficient of Variation, Normal Distribution, Pivotal Method, Confidence Interval.
by Eugenia Nissi, Annalina Sarra
The topic of this paper regards recent extensions of spatial scan statistics, widely used in public health research to test disease clusters and to identify their approximate locations. Despite its success, there is an important limitation associated with the traditional scan statistics: it depends on the use of circle shaped windows. As results, the identified regions are often not well localized. This limitation has motivated research aimed at developing new approaches which have the capability to detect clusters of irregular shapes. Two new techniques have been studied and compared: the spatial
scan statistics, based on the graph theory, and the flexible scan statistics which imposes an irregularly shaped window. A computational study has been carried out to evaluate the effectiveness of these new approaches. A better understanding of the relative strengths and weakness of these two methods is essential to appropriate choices of methodology.
Keywords: Detection Cluster Methods; Health Surveillance; Monte Carlo Testing; Simulated Annealing Scan Statistic; Flexible Scan Statistic.
by Mariateresa Cuoccio
In this paper we derive nonparametric Bayesian estimate for a real parameter of a measure which is, in its turn, the function parameter of a mixture of a Dirichlet process and credibility interval for the predictive value. We apply this approach to data of plant species of relative abundance as given by Alodat, Odat, Muhaidat and Beldjillali (2008).
Keywords: Power Function Distribution, Bayes Estimator, Prior Distribution, Plant Relatives abundance.