One of the main important issues of statistical analysis is forecasting. There are two important objectives of this article one is to developed time series modeling of stock market data of Karachi Stock Exchange (KSE-100). Second is to identify the probability distribution on banking sector of Karachi Stock Exchange (KSE-100).In this work, statistical relation between prices and corresponding t...

Following ideas of Richer (2000) we introduce the notion of unordered regressive Ramsey numbers or unordered Kanamori-McAloon numbers. We show that these are of Ackermannian growth rate. For a given number-theoretic function f we consider unordered f -regressive Ramsey numbers and classify exactly the threshold for f which gives rise to the Ackermannian growth rate of the induced unordered f -r...

and Applied Analysis 3 A function p : T → R is called regressive provided 1 μ t p t / 0 for all t ∈ T. The set of all regressive and rd-continuous functions p : T → R will be denoted by R R T R T,R . We define the set R R T,R {p ∈ R : 1 μ t p t > 0, for all t ∈ T}. An n × n-matrix-valued function A on a time scale T is called regressive provided I μ t A t is invertible ∀t ∈ T, 2.4 and the class...

Kanamori, A., Regressive partition relations, n-subtle cardinals, and Bore1 diagonalization, Annals of Pure and Applied Logic 52 (1991) 65-77. We consider natural strengthenings of H. Friedman’s Bore1 diagonahzation propositions and characterize their consistency strengths in terms of the n-subtle cardinals. After providing a systematic survey of regressive partition relations and their use in ...

Prediction distribution is a basis for predictive inferences applied in many real world situations. The Bayesian approach under uniform prior employed this paper to derive the prediction Simultaneous Auto-regressive model with multivariate Student-t error distribution. Conditional on set of realized responses, single and future responses have univariate distributions respectively, whose degrees...

A two-phased method for prediction in spatialtemporal domains is proposed. After an ordinary regression model is trained on spatial data, a prediction is adjusted by incorporating autoregressive modeling of residuals in time. The prediction accuracy of the proposed method is evaluated on simulated agricultural data with a significant improvement of accuracy for both linear and non-linear regres...