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111.
Paulo Canas Rodrigues Pétala G. S. E. Tuy Rahim Mahmoudvand 《Journal of Statistical Computation and Simulation》2018,88(10):1921-1935
ABSTRACTSingular spectrum analysis (SSA) is a relatively new method for time series analysis and comes as a non-parametric alternative to the classical methods. This methodology has proven to be effective in analysing non-stationary and complex time series since it is a non-parametric method and do not require the classical assumptions over the stationarity or over the normality of the residuals. Although SSA have proved to provide advantages over traditional methods, the challenges that arise when long time series are considered, make the standard SSA very demanding computationally and often not suitable. In this paper we propose the randomized SSA which is an alternative to SSA for long time series without losing the quality of the analysis. The SSA and the randomized SSA are compared in terms of quality of the model fit and forecasting, and computational time. This is done by using Monte Carlo simulations and real data about the daily prices of five of the major world commodities. 相似文献
112.
Paulo Canas Rodrigues Emilio Porcu 《Journal of Statistical Computation and Simulation》2018,88(10):1847-1849
113.
João D. F. Rodrigues 《商业与经济统计学杂志》2016,34(3):357-367
Empirical estimates of source statistical economic data such as trade flows, greenhouse gas emissions, or employment figures are always subject to uncertainty (stemming from measurement errors or confidentiality) but information concerning that uncertainty is often missing. This article uses concepts from Bayesian inference and the maximum entropy principle to estimate the prior probability distribution, uncertainty, and correlations of source data when such information is not explicitly provided. In the absence of additional information, an isolated datum is described by a truncated Gaussian distribution, and if an uncertainty estimate is missing, its prior equals the best guess. When the sum of a set of disaggregate data is constrained to match an aggregate datum, it is possible to determine the prior correlations among disaggregate data. If aggregate uncertainty is missing, all prior correlations are positive. If aggregate uncertainty is available, prior correlations can be either all positive, all negative, or a mix of both. An empirical example is presented, which reports relative uncertainties and correlation priors for the County Business Patterns database. In this example, relative uncertainties range from 1% to 80% and 20% of data pairs exhibit correlations below ?0.9 or above 0.9. Supplementary materials for this article are available online. 相似文献
114.
Josemar Rodrigues 《统计学通讯:理论与方法》2013,42(9):2165-2171
The aim of this paper is to propose a hierarchical Bayes approach and an appropriate loss function to perform a Bayesian analysis of the total number of software failures denoted by N. It is shown that the Bayes procedure is more stable than the maximum likelihood procedure and a stopping rule for debugging the software is suggested via the LIN EX loss function. 相似文献
115.
In this paper, we formulate a very flexible family of models which unifies most recent lifetime distributions. The main idea is to obtain a cumulative distribution function to transform the baseline distribution with an activation mechanism characterized by a latent threshold variable. The new family has a strong biological interpretation from the competitive risks point of view and the Box–Cox transformation provides an elegant manner to interpret the effect on the baseline distribution to obtain this alternative model. Several structural properties of the new model are investigated. A Bayesian analysis using Markov Chain Monte Carlo procedure is developed to illustrate with a real data the usefulness of the proposed family. 相似文献