Exact distributions of two non-central generalized Wilks's statistics

We derive the exact expressions of the probability density function (pdf) and the cumulative distribution function (cdf) of Wilks's likelihood ratio criterion Λ and Wilks-Lawley's statistic U in the non-central linear and the non-central planar cases. Those expressions are given in rapidly converging infinite series and can be used for numerical computation. For applications, we compute the exact power of these statistics in a multivariate analysis of variance exercise, and show by simulation the precision of our analytic formulae.     

Using multiple imputation to estimate cumulative distribution functions in longitudinal data analysis with data missing at random

In longitudinal clinical studies, after randomization at baseline, subjects are followed for a period of time for development of symptoms. The interested inference could be the mean change from baseline to a particular visit in some lab values, the proportion of responders to some threshold category at a particular visit post baseline, or the time to some important event. However, in some applications, the interest may be in estimating the cumulative distribution function (CDF) at a fixed time point post baseline. When the data are fully observed, the CDF can be estimated by the empirical CDF. When patients discontinue prematurely during the course of the study, the empirical CDF cannot be directly used. In this paper, we use multiple imputation as a way to estimate the CDF in longitudinal studies when data are missing at random. The validity of the method is assessed on the basis of the bias and the Kolmogorov–Smirnov distance. The results suggest that multiple imputation yields less bias and less variability than the often used last observation carried forward method. Copyright © 2013 John Wiley & Sons, Ltd.     

A survey of time series analysis through parametric models

The paper is a survey of recent works on time series analysis using parametric models. The main emphasis is on linear models, in particular the ARMA model. Usual me¬thods of parameter estimation, goodness of fit tests and the choice of model order are con¬sidered. Some extensions of the methods to related problems are briefly discussed     

Staying safe and visible via message sharing in online social networks

As an imperative channel for fast information propagation, online social networks (OSNs) also have their defects. One of them is the information leakage, i.e., information could be spread via OSNs to the users whom we are not willing to share with. Thus the problem of constructing a circle of trust to share information with as many friends as possible without further spreading it to unwanted targets has become a challenging research topic but still remained open. Our work is the first attempt to study the Maximum Circle of Trust problem seeking to share the information with the maximum expected number of poster’s friends such that the information spread to the unwanted targets is brought to its knees. First, we consider a special and more practical case with the two-hop information propagation and a single unwanted target. In this case, we show that this problem is NP-hard, which denies the existence of an exact polynomial-time algorithm. We thus propose a Fully Polynomial-Time Approximation Scheme (FPTAS), which can not only adjust any allowable performance error bound but also run in polynomial time with both the input size and allowed error. FPTAS is the best approximation solution one can ever wish for an NP-hard problem. We next consider the number of unwanted targets is bounded and prove that there does not exist an FPTAS in this case. Instead, we design a Polynomial-Time Approximation Scheme (PTAS) in which the allowable error can also be controlled. When the number of unwanted targets are not bounded, we provide a randomized algorithm, along with the analytical theoretical bound and inapproximaibility result. Finally, we consider a general case with many hops information propagation and further show its #P-hardness and propose an effective Iterative Circle of Trust Detection (ICTD) algorithm based on a novel greedy function. An extensive experiment on various real-world OSNs has validated the effectiveness of our proposed approximation and ICTD algorithms. Such an extensive experiment also highlights several important observations on information leakage which help to sharpen the security of OSNs in the future.     

Developments in implicit leadership theory and cognitive science: Applications to improving measurement and understanding alternatives to hierarchical leadership

After reviewing key findings regarding leadership categorization theory, we develop new perspectives regarding the design of behavioral measures of leadership and the implications of shared leadership and complex adaptive leadership conceptualizations of leadership. In particular, by applying recent developments in cognitive science, we explain how an understanding of symbolic, connectionist, and embodied representations of knowledge can benefit behavioral measures of leadership. Additionally, we address some practical issues associated with the measurement of leadership and argue that ratings which tap episodic memory at the event level may be more meaningful than ratings based on semantic memory. Finally, we discuss how notions of shared leadership and of leaders as catalysts for complexity can create unique complications for leadership perceptions, coordinated behavior within a group, and the measurement of leadership.     

A Combined D.C. Optimization—Ellipsoidal Branch-and-Bound Algorithm for Solving Nonconvex Quadratic Programming Problems

In this paper we propose a new branch-and-bound algorithm by using an ellipsoidal partition for minimizing an indefinite quadratic function over a bounded polyhedral convex set which is not necessarily given explicitly by a system of linear inequalities and/or equalities. It is required that for this set there exists an efficient algorithm to verify whether a point is feasible, and to find a violated constraint if this point is not feasible. The algorithm is based upon the fact that the problem of minimizing an indefinite quadratic form over an ellipsoid can be efficiently solved by some available (polynomial and nonpolynomial time) algorithms. In particular, the d.c. (difference of convex functions) algorithm (DCA) with restarting procedure recently introduced by Pham Dinh Tao and L.T. Hoai An is applied to globally solving this problem. DCA is also used for locally solving the nonconvex quadratic program. It is restarted with current best feasible points in the branch-and-bound scheme, and improved them in its turn. The combined DCA-ellipsoidal branch-and-bound algorithm then enhances the convergence: it reduces considerably the upper bound and thereby a lot of ellipsoids can be eliminated from further consideration. Several numerical experiments are given.     

More than just trees: Assessing reforestation success in tropical developing countries

Rural communities in many parts of the tropics are dependent of forests for their livelihoods and for environmental services. Forest resources in the tropics have declined rapidly over the past century and therefore many developing countries in the tropics have reforestation programs. Although reforestation is a long-term process with long-term benefits, existing evaluations of the success of these programs tends to focus on short-term establishment success indicators. This paper presents a review of reforestation assessment that highlights the need to not only consider short-term establishment success, but also longer-term growth and maturation success, environmental success and socio-economic success. In addition, we argue that reforestation assessment should not be based on success indicators alone, but should incorporate the drivers of success, which encompasses an array of biophysical, socio-economic, institutional and project characteristics. This is needed in order to understand the reasons why reforestation projects succeed or fail and therefore to design more successful projects in future. The paper presents a conceptual model for reforestation success assessment that links key groups of success indicators and drivers. This conceptual model provides the basis for a more comprehensive evaluation of reforestation success and the basis for the development of predictive systems-based assessment models. These models will be needed to better guide reforestation project planning and policy design and therefore assist rural communities in tropical developing countries to alleviate poverty and achieve a better quality of life.