We consider high dimensional nonhomogeneous linear regression models with p n 9 0 or p >> n, where p is the number of features and n is the number of observations. In the model considered, the underlying true regression coefficients undergo multiple changes. Our goal is to estimate the number and locations of these change-points and estimate sparse coefficients in each of the intervals between ...

In this study, we introduce a statistical model applied to climate change data consisting of an autoregressive times series (AR) which represents type random process. A Bayesian approach using MCMC (Markov Chain Monte Carlo) methods is considered get the inferences interest. The main goal study have good predictions for mean temperature and also identify time possible change-points that might b...

In this paper, using the best proximity theorems for an extensionof Brosowski's theorem. We obtain other results on farthest points. Finally, wedene the concept of e- farthest points. We shall prove interesting relationshipbetween the -best approximation and the e-farthest points in normed linearspaces (X; ||.||). If z in W is a e-farthest point from an x in X, then z is also a-best approximati...

this article which is aimed at regulating the change of laws in legislative system of islamic republic of iran, intends to shed light over a significant change which has recently been taken place in the inheritance law of iran. (articles 946-949 of civl law of iran). this is analyzed from various view points such as historical, political, social, economic, legal and juridical aspect. the probab...

Abstract:  In this study, the change in the crystalline structure of gas hydrate was predicted for ternary mixture of methane-ethane-water. For this purpose, the tangent plane distance (TPD) minimization method was used. First, the calculations were performed for the binary mixtures of methane-water and ethane-water as the gas and liquid phases. The results show that for a binary mixture of th...

Change-point models are generative models of time-varying data in which the underlying generative parameters undergo discontinuous changes at different points in time known as change points. Change-points often represent important events in the underlying processes, like a change in brain state reflected in EEG data or a change in the value of a company reflected in its stock price. However, ch...

Identifying short and long range change points in an observed time series that consists of stationary segments is a common problem. These change points mark the time boundaries of the segments where the time series leaves one stationary state and enters another. Due to certain technical advantages, analysis is carried out in the frequency domain to identify such change points in the time domain...