http://ccsenet.org/journal/index.php/ijsp/issue/feedInternational Journal of Statistics and Probability2014-10-30T18:23:31-07:00Wendy Smithijsp@ccsenet.orgOpen Journal Systems<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and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher.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><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>quarterly</strong> (February, May, August and November) 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" hspace="20" vspace="20" width="201" height="264" align="right" /><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><strong>DOAJ</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></ul>http://ccsenet.org/journal/index.php/ijsp/article/view/39453A Conditional Mean Square Estimate for the Solution of a SDE2014-10-27T18:13:10-07:00Andriy Yurachkivskyandriy.yurachkivsky@gmail.comLet $\bF=\left(\cF(t), \:t\in \bR_+\right)$ be a filtration on some probability space, and $X$ be the strong solution of the equation $X(t)=\X+\int_0^tQ(s,X(s))\rd\iota(s)+\int_0^t\sigma(s,X(s-)) \rd Y(s),$ where $\X$ is an $\cF(0)$-measurable $\bR^d$-valued random variable, $\iota$ is a continuous increasing process with $\cF(0)$-measurable values at all times, $Y$ is an $\bR^m$-valued locally square integrable martingale with respect to $\bF$ subjected to some mild additional demands, $Q$ and $\sigma$ are continuous in $x\in\bR^d$ random functions on $\bR_+\times\bR^d$ (the former $\bR^d$-valued and $\bF$-progressive in $(\omega,t)\in\Omega\times\bR_+$, the latter ($d\times m)$-matrix-valued and $\bF$-predictable). Suppose also that there exists an $\cF(0)\otimes\cB_+$-measurable in $(\omega,t)$ nonnegative random process $\psi$ such that, for all $t,x$, $x^\top Q(t,x) \le-\psi(t)|x|^2$ and $\int_0^t\psi(s)\rd\iota(s)<\infty.$ Under these assumptions, $\E(|X(t)|^2|\cF(0)$ is evaluated from above.2014-08-14T00:00:00-07:00http://ccsenet.org/journal/index.php/ijsp/article/view/37986Time Series Behavior of the Volume of Wood Products Export in Ghana2014-10-27T18:13:10-07:00Stella B. Acquahs_britwum@yahoo.comRichard K. Avuglahs_britwum@yahoo.comEmmanuel Harriss_britwum@yahoo.com<p>This study examines the patterns in the export of wood products in Ghana from 1997-2013. We also build a time series model to forecast the volume of wood products export over the same period. The study employs the Box-Jenkins methodology of building ARIMA (Autoregressive Integrated Moving Average) model. Monthly time series data on exports of wood products from 1997-2013 were extracted from monthly and annual reports on export of wood products published by the Timber Industry Development Division (TIDD) of the Forestry Commission of Ghana. Different selected models were tested to ensure the accuracy of obtained results and ARIMA (3, 1, 0) (0, 1, 1)_{12} was adjudged the best model. This model was then used to forecast the volume of wood products export for 2014 and 2015. January and June represent the minimum and maximum export periods respectively. The model will guide TIDD in their annual timber export planning and also help avoid financial losses that could result from poor decision making and ultimately improve efficiency of their operations.</p>2014-08-14T00:00:00-07:00http://ccsenet.org/journal/index.php/ijsp/article/view/38088Evaluating Variables as Unbiased Proxies for Other Measures: Assessing the Step Test Exercise Prescription as a Proxy for the Maximal, High-Intensity Peak Oxygen Consumption in Older Adults2014-10-27T18:13:10-07:00Jonathan D. Mahnkenjmahnken@kumc.eduXueyi Chenxchen@kumc.eduAlexandra R. Brownabrown8@kumc.eduEric D. Vidonievidoni@kumc.eduSandra A. Billingersbillinger@kumc.eduByron J. Gajewskibgajewski@kumc.eduTo assess validity of a low-intensity measure of fitness ($X$) in a population of older adults as a proxy measure for the original, high-intensity measure ($Y$), we used ordinary least square regression with the new, potential proxy measure ($X$) as the sole explanatory variable for $Y$. A perfect proxy measure would be unbiased (i.e., result in a regression line with a $y$-intercept of zero and a slope of one) with no error (variance equal to zero). We evaluated the properties of potential biases of proxy measures. A two degree-of-freedom approach using a contrast matrix in the setting of simple linear ordinary least squares regression was compared to a one degree-of-freedom paired $t$ test alternative approach. We found that substantial improvements in power could be gained through use of the two degree-of-freedom approach in many settings, while scenarios where no linear bias was present there could be modest gains from the paired $t$ test approach. In general, the advantages of the two degree-of-freedom approach outweighed the benefits of the one degree-of-freedom approach. Using the two degree-of-freedom approach, we assessed the data from our motivating example and found that the low-intensity fitness measure was biased, and thus was not a good proxy for the original, high-intensity measure of fitness in older adults.2014-09-23T22:16:44-07:00http://ccsenet.org/journal/index.php/ijsp/article/view/40592Testing Bivariate Normality Based on Nonlinear Canonical Analysis2014-10-27T18:13:10-07:00Mouhamed Amine Niangamine.niang@uadb.edu.snGuy Martial Nkietamine.niang@uadb.edu.snAliou Diopamine.niang@uadb.edu.snUsing a test statistic constructed on wavelets-based estimation of canonical coefficients of nonlinear canonical analysis, we introduce a new class for bivariate normality test. The limit distribution of the new test statistic is established. We also give some critical values of the distribution. The finite sample performance of the proposed test, with comparison to that of an existing method, is evaluated through Monte Carlo power study.2014-09-23T00:00:00-07:00http://ccsenet.org/journal/index.php/ijsp/article/view/41186Model Equivalence in General Linear Models: Set-to-Zero, Sum-to-Zero Restrictions, and Extra Sum of Squares Method2014-10-27T18:13:10-07:00Bashiru I. I. Saeedsirbash156@gmail.comS. K. Appiahsirbash156@gmail.comN. N. N. Nsowah Nuamahsirbash156@gmail.comLouis Munyakazisirbash156@gmail.comA. A. I. Musahsirbash156@gmail.comThe paper is drawn from the authors' experience in teaching general and generalized linear fixed effects models at the university level. The steps followed include model specification, model estimation, and hypothesis testing in general linear model setting. Among these steps, estimation of model parameters such as the main effect least squares means and contrasts were among the most challenging for students. Since no unique solution exists, students are first exposed to the equivalence between two popular techniques that an over-parameterized model can be subjected to in order to obtain the parameter estimates. This is particularly important because existing software do not necessarily follow the same path to produce an Analysis of Variance (or Covariance) of the general, generalized linear fixed or mixed effects models. These steps are generally hidden from the users. It is therefore crucial for the students to understand the intermediary processes that ultimately produce the same results regardless of the software one uses. The equivalent techniques, the set-to-zero and sum-to-zero restrictions, used to obtain solution of the normal equations of the fixed effects model, are presented. The relationship between them is also presented and in the process, data analysis makes use of two important concepts: the generalized inverse and estimable function. The invariance property of estimable functions is also explained in details in addition to the extra sum of squares principle which is introduced to supplement the other concepts. To exemplify these ideas and put them in practice, a simple one-way treatment structure analysis of variance is performed.<br /><br />2014-10-10T00:00:00-07:00http://ccsenet.org/journal/index.php/ijsp/article/view/39933Modified Quick Convergent Inflow Algorithm for Solving Linear Programming Problems2014-10-27T18:13:10-07:00M. P. Iwundumary_iwundu@yahoo.comD. W. Ebongmary_iwundu@yahoo.com<p>A Modified Quick Convergent Inflow Algorithm (MQCIA) for Solving Linear Programming problems, based on variance of predicted response, is presented. The method adds a point of maximum variance to an initial design thus leading to a maximizer of the response function in a maximization problem. Similarly, a point of minimum variance is added to an initial design thus leading to a minimizer of the response function in a minimization problem. Effectiveness of the method has been demonstrated and the results show that by improving an existing experimental design, the optimizer of the response function is approached. Analytical justification for the MQCIA has also been established.</p>2014-10-27T17:58:33-07:00http://ccsenet.org/journal/index.php/ijsp/article/view/39990On the Behaviour of D-Optimal Exact Designs Under Changing Regression Polynomials2014-10-27T18:13:10-07:00M. P. Iwundumary_iwundu@yahoo.comE. B. Albert-UdochukwukaRoland.Albert@saipem.comThe behaviour of D-optimal exact designs for first order polynomial models under changing regression polynomials is considered. The polynomials, some of which are with or without intercept or with or without interactive term, are defined on design regions that are supported by the points of the Circumscribed Central Composite Design. The best N-point D-optimal exact design for the intercept model (model 1), is the same as the best N-point D-optimal design for the no-intercept model (model 3). Similarly, the best N-point D-optimal design for the intercept model (model 2) is the same as the best N-point D-optimal design for the no-intercept model (model 4), as measured by the determinant values, D-efficiencies, G-efficiencies and Condition numbers. Other N-point designs constructed using the no-intercept models had better determinant values than their corresponding intercept models. The condition numbers indicate that for model 1, the 4-point D-optimal design is orthogonal. For model 2, the 2-point D-optimal exact design and the 4-point D-optimal exact designs are orthogonal. For model 3, the 4-point D-optimal exact design is orthogonal and for model 4, the 4-point D-optimal exact design is orthogonal. Other N-sized designs show less orthogonality. The Equivalence of D-optimality and G-optimality criteria is established for the 4-point design under model 1, for the 2-point and 4-point designs under model 2, for the 4-point design under model 3 and for the 4-point design under model 4.2014-10-27T18:11:45-07:00http://ccsenet.org/journal/index.php/ijsp/article/view/41821Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 3, No. 42014-10-30T18:23:31-07:00Wendy Smithijsp@ccsenet.org<p><em>International Journal of Statistics and Probability</em> wishes to acknowledge the following individuals for their assistance with peer review of manuscripts for this issue. Their help and contributions in maintaining the quality of the journal is greatly appreciated.</p> <p>Many authors, regardless of whether <em>International Journal of Statistics and Probability</em> publishes their work, appreciate the helpful feedback provided by the reviewers.</p> <p><strong>Reviewers for Volume 3, Number 4</strong></p> <p>Abdullah A. SMADI</p> <p>Anna Grana'</p> <p>Bibi Abdelouahab</p> <p>Carla J. Thompson</p> <p>Carolyn Huston</p> <p>Chin-Shang Li</p> <p>Ehsan Karim</p> <p>Enayetur Raheem</p> <p>Ivair R. Silva</p> <p>Jacek Bialek</p> <p>Jorge M. Mendes</p> <p>Kouji Yamamoto</p> <p>Lishu Li</p> <p>Michela Ottobre</p> <p>Philip Westgate</p> <p>Sajid Ali</p> <p>Sohair F. Higazi</p> <p>Vyacheslav Abramov</p> <p>Yichuan Zhao</p> <p>Zaixing Li</p> <p> </p> <p>Wendy Smith</p> <p>On behalf of,</p> <p>The Editorial Board of <em>International Journal of Statistics and Probability</em></p> <p>Canadian Center of Science and Education</p>2014-10-31T00:00:00-07:00