International Journal of Statistics and Probability
http://ccsenet.org/journal/index.php/ijsp
<em>International Journal of Statistics and Probability (IJSP) </em>is an open-access, international, double-blind peer-reviewed journal published by the <a href="/web/">Canadian Center of Science and Education</a>. <br /><br />This journal, published quarterly in both print and <a href="/journal/index.php/ijsp/issue/archive">online versions</a>, keeps readers up-to-date with the latest developments in all areas of statistics and probability.<br /><br />It is journal policy to publish work deemed by peer reviewers to be a coherent and sound addition to scientific knowledge and to put less emphasis on interest levels, provided that the research constitutes a useful contribution to the field.<br /><br />en-US<p>Submission of an article implies that the work described has not been published previously (except in the form of an abstract or as part of a published lecture or academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.</p><p><br />Copyrights for articles published in CCSE journals are retained by the authors, with first publication rights granted to the journal. The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.</p>ijsp@ccsenet.org (Wendy Smith)ijsp@ccsenet.org (Technical Support)Wed, 11 Jun 2014 00:23:00 -0700OJS 2.3.8.0http://blogs.law.harvard.edu/tech/rss60Asymptotic Efficiency of an Exponential Cure Model When Cure Information Is Partially Known
http://ccsenet.org/journal/index.php/ijsp/article/view/34438
Cure models are popularly used to analyze failure time data where some individuals could eventually experience and others might never experience an event of interest. However in many studies, there are diagnostic procedures available to provide further information about whether a subject is cured. Wu et al. (2014) proposed a method, called the {\it extended} cure model, that incorporated such additional diagnostic cured status information into the classical cure model analysis. Through extensive simulations, they demonstrated that the extended cure models provide more efficient and less biased estimations, and higher efficiency and smaller bias are associated with higher sensitivity and specificity of the diagnostic procedure used. In this paper, we provide theoretical justifications of this positive association for some special cases. More specifically we shows that the maximum likelihood estimators (MLEs) of the parameters for an extended exponential cure model are asymptotically more efficient than the MLEs for the corresponding classical exponential cure model.Yu Wu, Yong Lin, Chin-Shang Li, Shou-En Lu, Weichung Joe Shihhttp://ccsenet.org/journal/index.php/ijsp/article/view/34438Wed, 11 Jun 2014 00:04:06 -0700Bayesian Approach Using Latent Variable for Zero Truncated Poisson Distribution: Application for Species-Area Relationship
http://ccsenet.org/journal/index.php/ijsp/article/view/35902
In ecology, understanding the species-area relationship (SARs) is extremely important to determine species diversity. SARs are fundamental to evaluate the impact in this diversity due to destruction of natural habitats, to create biodiversity maps and to determine the minimum area to preserve. In this study, the number of species is observed in different area sizes. These studies are referred in the literature through nonlinear models without assuming any distribution of the data. In this situation, it only makes sense to consider areas in which the number of species is greater than zero. As the dependent variable is a count data, we assume that this variable comes from a known distribution for discrete positive data. In this paper, we used the zero truncated poisson distribution (ZTP) to represent the probability distribution of the random variable ``species diversity" and we considered some nonlinear models to describe the relationship between species diversity and habitat area. Among the proposed models in literature, we considered the Arrhenius power function, Persistence function (P1 e P2), Negative Exponential and Chapman-Richards to describe the abundance of species. In this paper, we take a Bayesian approach to fit models. With the purpose of obtaining conditional distributions, we propose the use of latent variables to implement the Gibbs Sampler. In order to progress using the best possible models for data, a comparison of performance between models referred in this paper will be verified through the criteria Extended Akaike Information Criterion (EAIC), Extended Bayesian Information Criterion (EBIC), Deviance Information Criterion (DIC) and Conditional Predictive Ordinate Criterion (CPO). In addition to selecting the best model, it will also assist to define the best selection criterion.Claude Thiago Arrabal, Marinho Gomes de Andrade Filho, Karina Paula dos Santos Silvahttp://ccsenet.org/journal/index.php/ijsp/article/view/35902Wed, 11 Jun 2014 00:07:15 -0700The Parameters Optimization of Filtered Derivative for Change Points Analysis
http://ccsenet.org/journal/index.php/ijsp/article/view/37770
Let $\mathbf{X} = ( X_1,X_2,\ldots,X_N )$ be a time series. That is a sequence random variable indexed by the time $t=(1,2,\ldots,N)$, we suppose that the parameters of $\mathbf{X}$ are piecewise constant. In other words, it exists a subdivision $\tau=(\tau_1< \tau_2<\ldots < \tau_K )$ such that $ X_i$ is a family of independent and identically distributed (i.i.d) random variables for $i \in (\tau_k,\tau_{k+1}] $, and $k = 0,1,\ldots,K$ where by convention $\tau_o=0$ and $\tau_{K+1}=N $. The preceding works such that (Bertrand, 2000) control the probability of false alarms for minimizing the probability of type I error of change point analysis. The novelty in this work is to control the number of false alarms. We give an bound of number of false alarms and the necessary condition for number of no detection. In other hand, we know the filtered derivative (Basseville \& Nikirov, 1993) depends the parameters such that the threshold and the window, we give in order to choose the optimal parameters. We compare the results of Filtered Derivative optimized parameters and the Penalized Square Error methods in particulary the adaptive method of (Lavielle \& Teyssi\`ere, 2006).Mohamed Elmihttp://ccsenet.org/journal/index.php/ijsp/article/view/37770Wed, 11 Jun 2014 00:00:00 -0700Approximate Nonparametric Maximum Likelihood Estimation for Interval Censoring Model Case II (Running Head: NPMLE for Interval Censoring Case II)
http://ccsenet.org/journal/index.php/ijsp/article/view/36130
We study the nonparametric maximum likelihood estimate of the distribution function in a type II interval censoring model. We propose an approximate solution of the problem under a technical assumption. Some basic asymptotic properties of the estimator are investigated.<br />Ao Yuan, Yizheng Wei, Kepher Makambi, Hongkun Wanghttp://ccsenet.org/journal/index.php/ijsp/article/view/36130Wed, 11 Jun 2014 00:14:34 -0700A Pathwise Fractional One Compartment Intra-Veinous Bolus Model
http://ccsenet.org/journal/index.php/ijsp/article/view/38179
To extend the deterministic compartments pharmacokinetics models as diffusions seems not realistic on the biological side because the paths of these stochastic processes are not smooth enough. In order to extend the one compartment intra-veinous bolus models, this paper suggests to model the concentration process $C$ by a class of stochastic differential equations driven by a fractional Brownian motion of Hurst parameter belonging to $]1/2,1[$.<br /><br />The first part of the paper provides probabilistic and statistical results on the concentration process $C$: the distribution of $C$, a control of the uniform distance between $C$ and the solution of the associated ordinary differential equation, and consistent estimators of the elimination constant, of the Hurst parameter of the driving signal, and of the volatility constant.<br /><br />The second part of the paper provides applications of these theoretical results on simulated concentrations: a method to choose the parameters on small sets of observations, and simulations of the estimators of the elimination constant and of the Hurst parameter of the driving signal. The relationship between the quality of the estimations and the size/length of the sample is discussed.Nicolas Mariehttp://ccsenet.org/journal/index.php/ijsp/article/view/38179Thu, 26 Jun 2014 00:00:00 -0700Statistical Inference for a Simple Constant Stress Model Based on Censored Sampling Data From the Kumaraswamy Weibull Distribution
http://ccsenet.org/journal/index.php/ijsp/article/view/38180
In this paper, constant stress accelerated life tests are discussed based on Type I and Type II censored sampling data from Kumaraswmay Weibull distribution. The maximum likelihood estimators are derived for the unknown parameters. The log linear model is assumed as an accelerated model. In addition, confidence intervals for the model parameters are constructed. Optimum test plans, are developed to minimize the generalized asymptotic variance of the maximum likelihood estimators of the model parameters. Monte Carlo simulation is carried out to illustrate the theoretical results of the maximum likelihood estimates, confidence intervals and optimum test plans.G. R. AL-Dayian, A. A. EL-Helbawy, H. R. Rezkhttp://ccsenet.org/journal/index.php/ijsp/article/view/38180Thu, 26 Jun 2014 00:00:00 -0700A Simulation Study Comparing Knot Selection Methods With Equally Spaced Knots in a Penalized Regression Spline
http://ccsenet.org/journal/index.php/ijsp/article/view/36992
<span style="color: #000000;"></span>Penalized regression splines are a commonly used method to estimate complex non-linear relationships between two variables. The fit of a penalized regression spline to the data depends on the number of knots, knot placement, and the value of the smoothing parameter. In this paper, we use a simulation study to compare knot selection methods with equidistant knots in a penalized regression spline model. We found that one method generally performed better than others. The results provide guidance in selecting the number of equidistant knots in a penalized regression spline model.Eduardo L. Montoya, Nehemias Ulloa, Victoria Millerhttp://ccsenet.org/journal/index.php/ijsp/article/view/36992Thu, 26 Jun 2014 01:14:54 -0700Marginal Methods for Multivariate Failure Times Under Event-Dependent Censoring
http://ccsenet.org/journal/index.php/ijsp/article/view/37529
Many chronic diseases put individuals at increased risk of several different types of adverse clinical events. Typically these events are combined to define composite events which are then used as the basis of treatment evaluation. A potentially more efficient approach is to conduct separate marginal assessments of the effect of treatment on each component and then to synthesize this information across each type of event. While there is considerable potential for more powerful tests of treatment effect in this setting, it is possible that dependent censoring can cause problems. This happens when the occurrence of one type of event increases the risk of withdrawal from a study and hence alters the probability of observing events of other types. The purpose of this article is to formulate a model which reflects this type of mechanism, to evaluate the effect on the asymptotic and finite sample properties of marginal estimates, and to examine the performance of estimators obtained using flexible inverse probability weighted marginal estimating equations. Data from a motivating study are used for illustration.Longyang Wu, Richard J. Cookhttp://ccsenet.org/journal/index.php/ijsp/article/view/37529Thu, 03 Jul 2014 01:20:33 -0700Modeling Event Clustering Using the m-Memory Cox-Type Self-Exciting Intensity Model
http://ccsenet.org/journal/index.php/ijsp/article/view/38984
In the analysis of point processes or recurrent events, the self-exciting component can be an important factor in understanding and predicting event occurrence. A Cox-type self-exciting intensity point process is generally not a proper model because of its explosion in finite time. However, the model with $m$-memory is appropriate to analyze sequences of recurrent events. It assumes the most recent $m$ events multiplicatively affect the conditional intensity of event occurrence. Aside from the interpretability, one advantage is the simplicity of the estimation and inference--the Cox partial likelihood can be applied and the resulting estimator is consistent and asymptotically normal. Another advantage is that the model can be applied to the analysis of case-cohort data via the pseudo-likelihood approach. The simulation studies support the asymptotic theory. Application is illustrated with analysis of a bladder cancer dataset and of an Australian stock index dataset, which shows evidence of self-excitation.Feng Chen, Kani Chenhttp://ccsenet.org/journal/index.php/ijsp/article/view/38984Mon, 28 Jul 2014 00:00:00 -0700Some New Characterizations of Markov-Bernoulli Geometric Distribution Related to Random Sums
http://ccsenet.org/journal/index.php/ijsp/article/view/38009
<p>The Markov-Bernoulli geometric distribution is obtained when a generalization, as a Markov process, of the independent Bernoulli sequence of random variables, is introduced. In this paper, new characterizations of the Markov-Bernoulli geometric distribution, as the distribution of the summation index of randomly truncated non-negative integer valued random variables, are given in terms of moment relations of the sum and summands. The achieved results generalize the corresponding characterizations concerning the usual geometric distribution.</p>M. Gharib, M. M. Ramadan, Kh. A. H. Al-Ajmihttp://ccsenet.org/journal/index.php/ijsp/article/view/38009Mon, 28 Jul 2014 00:04:59 -0700Comparing Decision Tree Method Over Three Data Mining Software
http://ccsenet.org/journal/index.php/ijsp/article/view/37872
As a result of the growing IT and producing methods and collecting data, it is admitted that today the data can be warehoused faster in comparison with the past. Therefore, knowledge discovery tools are required in order to make use of data mining. Data mining is typically employed as an advanced tool for analyzing the data and knowledge discovery. Indeed, the purpose of data mining is to establish models for decision. These models have the ability to predict the future treatments according to the past analysis and are of the exciting areas of machine learning and adaptive computation. Statistical analysis of the data uses a combination of techniques and artificial intelligence algorithms and data quality information. To utilize the data mining applications, including the commercial and open source applications, numerous programs are currently available.<br /><br />In this research, we introduce data mining and principal concepts of the decision tree method which are the most effective and widely used classification methods. In addition, a succinct description of the three data mining software, namely \textit{SPSS-Clementine}, \textit{RapidMiner} and \textit{Weka} is also provided. Afterwards, a comparison was performed on 3515 real datasets in terms of classification accuracy between the three different decision tree algorithms in order to illustrate the procedure of this research. The most accurate decision tree algorithm is \emph{Decision Tree} by 92.49\% in \emph{Rapidminer}.Ida Moghimipour, Malihe Ebrahimpourhttp://ccsenet.org/journal/index.php/ijsp/article/view/37872Mon, 28 Jul 2014 00:04:56 -0700Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 3, No. 3
http://ccsenet.org/journal/index.php/ijsp/article/view/38987
Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 3, No. 3, 2014Wendy Smithhttp://ccsenet.org/journal/index.php/ijsp/article/view/38987Mon, 28 Jul 2014 00:00:00 -0700