Construction Of Random Variables Research Articles

A kind of asymptotic properties of moving averages for Markov chains in Markovian environments

ABSTRACTConsider a Markov chain with finite alphabets. In this paper, we study the asymptotic properties of moving average, harmonic mean, and strong deviation theorems (limit theorems expressed by inequalities) of moving geometric average of random transition probabilities and the generalized entropy ergodic theorem for Markov chains in single infinite Markovian environments. It is shown that, under some mild conditions, the sequence of the generalized relative entropy density converges almost surely and in . The trick of the proofs is the construction of random variables with a parameter and the application of Borel–Cantelli lemma.

The Extended Skew-Exponential Power Distribution and Its Derivation

We consider an extended family of asymmetric univariate distributions generated using a symmetric density, f, and the cumulative distribution function, G, of a symmetric distribution, which depends on two real-valued parameters λ and β and is such that when β = 0 it includes the entire class of distributions with densities of the form g(z | λ) = 2 G(λ z) f(z). A key element in the construction of random variables distributed according to the family is that they can be represented stochastically as the product of two random variables. From this representation we can readily derive theoretical properties, easy-to-implement simulation schemes, as well as extensions to the multivariate case and an explicit procedure for obtaining the moments. We give special attention to the extended skew-exponential power distribution. We derive its information matrix in order to obtain the asymptotic covariance matrix of the maximum likelihood estimators. Finally, an application to a real data set is reported, which shows that the extended skew-exponential power model can provide a better fit than the skew-exponential power distribution.

Construction of random variables according to the probabilities of their binary digits

One states special properties of binary expansions of random variables, having a “truncated” geometric (discrete uniform) and exponential (uniform) distributions, which simplify in an essential manner the construction of random variables according to the probabilities of their binary digits.