Class Of Normal Distributions Research Articles

A New Nonlinear Choquet-Like Integral With Applications in Normal Distributions Based on Monotone Measures

In the theory of fuzzy measures, Choquet integral is one of the most important tools. The calculation of Choquet integral on real line is difficult for many cases such as nonmonotone functions. In this paper, by the geometric interpretation of Choquet integral, we introduce a new Choquet-like integral with a different algebraic interpretation of Choquet integral on real line. The calculation of this integral is simpler than Choquet integral on real line. Based on this integral, we introduce a general class of normal distribution on monotone measures. Finally, as an application, the real dataset obtained from the daily price of Dow Jones Industrial Average Index in period of June 2, 2008 to June 2, 2018 is analyzed.

Bounds for characteristic functions in terms of quantiles and entropy

Upper bounds on characteristic functions are derived in terms of the entropic distance to the class of normal distributions.

Semiparametric Bayesian Inference in Autoregressive Panel Data Models

This paper develops semiparametric Bayesian methods for inference in dynamic linear panel data models, and applies them to longitudinal data on labor earnings from the Panel Study of Income Dynamics. We focus on characterizing not only parameters related to conditional means and variances, but the entire joint distribution of earnings. Full distributional inference in semiparametric panel data models must solve a nonparametric deconvolution problem arising from the existence of unobserved additive individual-specific terms. A computational Bayesian approach based on variable augmentation can deal with such latent-variable models effectively, and can allow considerable flexibility in distributional assumptions. We use relatively simple, low-order linear models with individual heterogeneity, but estimate the distribution of the disturbances without requiring that it belong to a restrictive parametric class, such as the class of normal distributions. A prior distribution on the space of probability distributions is used to impose smoothness on an otherwise general specification for the disturbance terms. This nonparametric prior is constructed using a countable mixture of normals representation, where the mixing distribution is given a Dirichlet process prior.

Bayesian robust experimental designs for the one-way analysis of variance

In this paper, a simple, easily implemented procedure for the derivation of a robust Bayesian experimental design and estimator is proposed. The model is a one-way analysis of variance where the data vector is normally distributed. The prior distribution of the treatment means belongs to a particular class of normal distributions with a fixed prior mean and a set of possible covariance matrices.

A Case Study of a Versatile Generator of Repeatable Non-Poisson Sequences of Pseudorandom Pulses

This paper is a case study of a general method for designing generators of repeatable non-Poisson sequences of pseudorandom pulses. A class of normal distributions and some classes of skew distributions are derived. Block diagrams and design guides are given. The study is based both on analytical and simulation approaches. Applications of the proposed system may be found in the field of hardware simulation techniques and in more efficient arrangements of general purpose machines where they are used in simulation studies.

Zur Unverf�lschtheit des Pitman-Tests auf positive stochastische Abh�ngigkeit

Let $$\mathfrak{F}_p (\mathfrak{F}_n )$$ be the class of all two dimensional continuous distributions with positive (negative) likelihood ratio dependence. It is shown that the Pitman permutation test for testing independence against positive likelihood ratio dependence at level α is unbiased and of level α for $$\mathfrak{F}_n $$ . Furthermore, it is optimal against the class of normal distributions with positive correlation coefficient ρ.