Bivariate Semi-Logistic Distribution and Processes

application of crovelli's bivariate gamma distribution in hydrological processes

Mathematical methods and statistical distributions present exact results in the climate calculations and hydrological processes. Awareness of the rainfall probability distribution provides the appropriate conditions for water resource planning. Many studies have been done to estimate probability of rainfall by various methods due to the importance of rainfall distribution in the economic, soci...

Semi Supervised Logistic Regression

Semi-supervised learning has recently emerged as a new paradigm in the machine learning community. It aims at exploiting simultaneously labeled and unlabeled data for classification. We introduce here a new semi-supervised algorithm. Its originality is that it relies on a discriminative approach to semisupervised learning rather than a generative approach, as it is usually the case. We present ...

Estimation of Parameters of Morgenstern Type Bivariate Logistic Distribution by Ranked Set Sampling

The concept of ranked set sampling was first introduced by McIntyre (1952) as a process of improving the precision of the sample mean as an estimator of the population mean. This is applicable whenever ranking of a set of sampling units can be done easily by a judgement method see, Chen et al. (2004). Ranking by judgement method is not recommendable if the judgement method is not mathematically...

The Bivariate Marginal Distribution

Bivariate Uniqueness and Endogeny for the Logistic Recursive Distributional Equation

In this article we prove the bivariate uniqueness property for a particular “max-type” recursive distributional equation (RDE). Using the general theory developed in [5] we then show that the corresponding recursive tree process (RTP) has no external randomness, more preciously, the RTP is endogenous. The RDE we consider is so called the Logistic RDE, which appears in the proof of the ζ(2)-limi...

The Bivariate Normal Distribution

Let U and V be two independent normal random variables, and consider two new random variables X and Y of the form X = aU + bV, Y = cU + dV, where a, b, c, d, are some scalars. Each one of the random variables X and Y is normal, since it is a linear function of independent normal random variables.† Furthermore, because X and Y are linear functions of the same two independent normal random variab...