In this paper, we propose an alternative generalization of a recent test for univariate normality which is based on the empirical moment generating function to the multivariate case. We show, among other properties, that the proposed weighted L2-class of statistics is affine invariant and consistent. The empirical critical values of the proposed test are evaluated for different sample sizes, variable dimensions and values of the smoothing parameter through large scale simulations. The empirical power comparison of the test with a strong competitor shows that the test has a considerably high power performance, especially at large sample sizes as well as under heavy-tailed alternative distributions. The application of the statistic, together with its competitor, to six real-life datasets also supports the considerable good power performance of the proposed statistic as well as its ease of application.

There are several methods for goodness of fit test for the skew normal distribution. This work focused on method of Meintanis [8] which is based on the empirical moment generating function. This test is discussed for the known and the unknown shape parameter. Meintanis [8] claimed that power of his test is higher than the Kolmogorov-Smirnov test. But this claim is true only for the known shape parameter. In this paper, we provide a new method for finding his test statistic that has more efficiency. Also Meintanis [8] not determine the size of himself test for the known shape parameter which in this paper we will determine it.

We obtain the generating function for the number of columns of fixed height $r$ in a bargraph (classified according to semi-perimeter). As initial case for two distinct methods we first find the generating function for columns of height $1$. Then using a first-return-to-level-$1$ decomposition, we obtain the rational function version of the continued fraction generating function which allows us to derive separate recursions for its numerator and denominator. This then allows us to get the asymptotic average number of columns for each $r$. We also obtain an equivalent generating function by exploiting a sequential decomposition for bargraphs in terms of columns of height $r$.

In this note we propose an extended skew-Laplace distribution. We obtain explicit expressions for moment generating function and the two first moments of this distribution. Next, we show that the distribution of a linear combination of order statistics from a bivariate Laplace distribution can be expressed as a mixture of extended skew-Laplace distributions. This mixture representation enables us to derive moment generating function and moments of this linear combination.

In this study, a generalization of the Pell sequence called bi-periodic Pell sequence is carried out to matrix theory. Therefore, we call this matrix sequence the bi-periodic Pell matrix sequence whose entries are bi-periodic Pell numbers. Then the generating function, Binet formula and some basic properties and sum formulas are examined.

The present ideology of neo-classical economic paradigm to follow their own interests and are destroying the ethical basis and its consequences, ethical virtuous society is low. Lean thinking judgment based on knowledge of human capital to "do the right thing" is not merely to "do it right". Doing the right things with just the ethical value management by humans as creatures is possible. Therefore, attention to the concept of ethical capital to create value beyond the existing mission and values will be doubled. In the present article aims to identify ethical capital, and reviewed different views on this issue.

In this paper, a new generalization of the half-normal distribution which is called the perturbed half-normal distribution is introduced. The new distribution belongs to a family of distributions which includes the half-normal distribution along with an extra parameter to regulate skewness. The probability density function (pdf) is derived and some various properties of the new distribution are obtained. The derived properties include the cumulative distribution function (cdf), the rth moment, moment generating function, characteristic function, mean deviation about the mean and estimation of the parameters using the method of moments and maximum likelihood. Finally, the flexibility and potentiality of the new distribution is illustrated in an application to two real data sets.