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and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher.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, 13 Aug 2014 23:22:57 -0700OJS 2.3.8.0http://blogs.law.harvard.edu/tech/rss60A Conditional Mean Square Estimate for the Solution of a SDE
http://ccsenet.org/journal/index.php/ijsp/article/view/39453
Let $\bF=\left(\cF(t), \:t\in \bR_+\right)$ 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.Andriy Yurachkivskyhttp://ccsenet.org/journal/index.php/ijsp/article/view/39453Thu, 14 Aug 2014 00:00:00 -0700Time Series Behavior of the Volume of Wood Products Export in Ghana
http://ccsenet.org/journal/index.php/ijsp/article/view/37986
<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>Stella B. Acquah, Richard K. Avuglah, Emmanuel Harrishttp://ccsenet.org/journal/index.php/ijsp/article/view/37986Thu, 14 Aug 2014 00:00:00 -0700Evaluating 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 Adults
http://ccsenet.org/journal/index.php/ijsp/article/view/38088
To 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.Jonathan D. Mahnken, Xueyi Chen, Alexandra R. Brown, Eric D. Vidoni, Sandra A. Billinger, Byron J. Gajewskihttp://ccsenet.org/journal/index.php/ijsp/article/view/38088Tue, 23 Sep 2014 22:16:44 -0700Testing Bivariate Normality Based on Nonlinear Canonical Analysis
http://ccsenet.org/journal/index.php/ijsp/article/view/40592
Using 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.Mouhamed Amine Niang, Guy Martial Nkiet, Aliou Diophttp://ccsenet.org/journal/index.php/ijsp/article/view/40592Tue, 23 Sep 2014 00:00:00 -0700Model Equivalence in General Linear Models: Set-to-Zero, Sum-to-Zero Restrictions, and Extra Sum of Squares Method
http://ccsenet.org/journal/index.php/ijsp/article/view/41186
The 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 />Bashiru I. I. Saeed, S. K. Appiah, N. N. N. Nsowah Nuamah, Louis Munyakazi, A. A. I. Musahhttp://ccsenet.org/journal/index.php/ijsp/article/view/41186Fri, 10 Oct 2014 00:00:00 -0700