International Journal of Statistics and Probability
http://ccsenet.org/journal/index.php/ijsp
<em><strong>International Journal of Statistics and Probability</strong> </em>(ISSN: 1927-7032; E-ISSN: 1927-7040) is an open-access, international, double-blind peer-reviewed journal published by the Canadian Center of Science and Education. This journal, published <strong>bimonthly</strong> (<span>January, March, May, July, September and November</span>) in both<strong> print and online versions</strong>, keeps readers up-to-date with the latest developments in all areas of statistics and probability.<img src="/journal/public/site/images/ijsp/ijsp.jpg" alt="ijsp" width="201" height="264" align="right" hspace="20" vspace="20" /><p><strong>The scopes of the journal </strong>include, but are not limited to, the following topics: computational statistics, design of experiments, sample survey, statistical modelling, statistical theory, probability theory.</p><p>This journal accepts article submissions<strong> <a href="/journal/index.php/ijsp/information/authors">online</a> or by <a href="mailto:ijsp@ccsenet.org">e-mail</a> </strong>(ijsp@ccsenet.org).</p><p><strong><strong>ABSTRACTING AND INDEXING:</strong></strong></p><ul><li>BASE (Bielefeld Academic Search Engine)<strong><br /></strong></li><li><strong>EBSCOhost</strong></li><li>Google Scholar</li><li>JournalTOCs</li><li>Library and Archives Canada</li><li>LOCKSS</li><li>PKP Open Archives Harvester</li><li><strong>ProQuest</strong></li><li>SHERPA/RoMEO</li><li>Standard Periodical Directory</li><li><strong>Ulrich's</strong></li></ul>Canadian Center of Science and Educationen-USInternational Journal of Statistics and Probability1927-7032<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and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher.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>On Consistency of Absolute Deviations Estimators of Convex Functions
http://ccsenet.org/journal/index.php/ijsp/article/view/56476
When estimating an unknown function from a data set of n observations, the function is often known to be convex. For example, the long-run average waiting time of a customer in a single server queue is known to be convex in the service rate (Weber 1983) even though there is no closed-form formula for the mean waiting time, and hence, it needs to be estimated from a data set. A computationally efficient way of finding the best fit of the convex function to the data set is to compute the least absolute deviations estimator minimizing the sum of absolute deviations over the set of convex functions. This estimator exhibits numerically preferred behavior since it can be computed faster and for a larger data sets compared to other existing methods (Lim & Luo 2014). In this paper, we establish the validity of the least absolute deviations estimator by proving that the least absolute deviations estimator converges almost surely to the true function as n increases to infinity under modest assumptions.Yao LuoEunji Lim
Copyright (c) 2016 International Journal of Statistics and Probability
2016-02-092016-02-0952110.5539/ijsp.v5n2p1On Predicting Survival in Prostate Cancer: Using an Extended Maximum Spacing Method at the Change Point of the Semiparametric Ratio Estimator (SPRE)
http://ccsenet.org/journal/index.php/ijsp/article/view/57236
<p>Prostate cancer is a condition of public health significance in the United States. A new method for predicting survival is derived for the domain around the change point from a semiparametric ratio estimator (SPRE) to predict survival in response to treatment for prostate cancer. Using an extended maximum spacing estimator, the geometric mean of sample spacings from a uniform distribution <span style="font-family: Times New Roman; font-size: medium;"> is derived </span>with known endpoints given at 0 and at the value of the change point from an ordinary least squares (OLS) regression for SPRE. To determine the maximum interval on the ‘x’ axis between point estimates, the maximum spacing estimation method is derived from a continuous univariate distribution where spacing will be defined as gaps between ordered values of the distribution function. The maximum is defined as a single value in the neighborhood of the change point and spacing defined as a function of time. This maximum spacing defines the gaps between point estimates at each time-dependent predicted outcome from the change point and results in a semiparametric ratio estimator that is reliable and repeatable. Performance is discussed through a simulation of change point values for a real application in clinical medicine and, using SPRE, in personalized medicine for a single prostate cancer patient.</p>Deborah Weissman-Miller
Copyright (c) 2016 International Journal of Statistics and Probability
2016-02-102016-02-10521910.5539/ijsp.v5n2p19On the Convergence Rate for a Kernel Estimate of the Regression Function
http://ccsenet.org/journal/index.php/ijsp/article/view/57237
We give the rate of the uniform convergence for the kernel estimate of the regression function over a sequence of compact sets which increases to $\mathbb{R}^{d}$ when $n$ approaches the infinity and when the observed process is $\varphi$-mixing. The used estimator for the regression function is the kernel estimator proposed by Nadaraya, Watson (1964).Mounir ARFI
Copyright (c) 2016 International Journal of Statistics and Probability
2016-02-102016-02-10522910.5539/ijsp.v5n2p29Transmetric Density Estimation
http://ccsenet.org/journal/index.php/ijsp/article/view/57238
<div>A transmetric is a generalization of a metric that is tailored to properties needed in kernel density estimation. Using transmetrics in kernel density estimation is an intuitive way to make assumptions on the kernel of the distribution to improve convergence orders and to reduce the number of dimensions in the graphical display. This framework is required for discussing the estimators that are suggested by Hovda (2014).</div><div> </div><div>Asymptotic arguments for the bias and the mean integrated squared error is difficult in the general case, but some results are given when the transmetric is of the type defined in Hovda (2014). An important contribution of this paper is that the convergence order can be as high as $4/5$, regardless of the number of dimensions.</div>Sigve Hovda
Copyright (c) 2016 International Journal of Statistics and Probability
2016-02-102016-02-10523510.5539/ijsp.v5n2p35