Springer Science+Business Media
About: Metrika is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Estimator & Asymptotic distribution. It has an ISSN identifier of 0026-1335. Over the lifetime, 2466 publications have been published receiving 29925 citations. The journal is also known as: Metrika (Heidelberg. Print).
Papers published on a yearly basis
TL;DR: A partial correlation graph for time series is defined and the partial spectral coherence between two components given the remaining components to identify the edges of the graph is used.
Abstract: In this paper we extend the concept of graphical models for multivariate data to multivariate time series. We define a partial correlation graph for time series and use the partial spectral coherence between two components given the remaining components to identify the edges of the graph. As an example we consider multivariate autoregressive processes. The method is applied to air pollution data.
TL;DR: In this paper, the existence and uniqueness of Q-functions are discussed. But they focus on transition functions and do not address the uniqueness problem in the context of transition functions.
Abstract: Contents: Transition Functions and Resolvents.- Existence and Uniqueness of Q-Functions.- Examples of Continuous Time Markov Chains.- More on the Uniqueness Problem.- Classification of States and Invariant Measures.- Strong and Exponential Ergodicity.- Reversibility, Monotonictity, and Other Properties.- Birth and Death Processes.- Population Processes.- Bibliography.- Symbol Index.- Author Index.- Subject Index.
TL;DR: In this article, the estimation of P[Y < X] when X and Y are two independent generalized exponential distributions with different shape parameters but having the same scale parameters is dealt with.
Abstract: This paper deals with the estimation of P[Y < X] when X and Y are two independent generalized exponential distributions with different shape parameters but having the same scale parameters. The maximum likelihood estimator and its asymptotic distribution is obtained. The asymptotic distribution is used to construct an asymptotic confidence interval of P[Y < X]. Assuming that the common scale parameter is known, the maximum likelihood estimator, uniformly minimum variance unbiased estimator and Bayes estimator of P[Y < X] are obtained. Different confidence intervals are proposed. Monte Carlo simulations are performed to compare the different proposed methods. Analysis of a simulated data set has also been presented for illustrative purposes.