This paper focuses on the class of finite-state, discrete-index, reciprocal processes (reciprocal chains). Such a class of processes seems to be a suitable setup in many applications and, in particular, it appears well-suited for image-processing. While addressing this issue, the aim is 2-fold: theoretic and practical. As to the theoretic purpose, some new results are provided: first, a general stochastic realization result is provided for reciprocal chains endowed with a known, arbitrary, distribution. Such a model has the form of a fixed-degree, nearest-neighbour polynomial model. Next, the polynomial model is shown to be exactly linearizable, which means it is equivalent to a nearest-neighbour linear model in a different set of variables. The latter model turns out to be formally identical to the Levi–Frezza–Krener linear model of a Gaussian reciprocal process, although actually non-linear with respect to the chain's values. As far as the practical purpose is concerned, in order to yield an example of application an estimation issue is addressed: a suboptimal (polynomial-optimal) solution is derived for the smoothing problem of a reciprocal chain partially observed under non-Gaussian noise. To this purpose, two kinds of boundary conditions (Dirichlet and Cyclic), specifying the reciprocal chain on a finite interval, are considered, and in both cases the model is shown to be well-posed, in a ‘wide-sense’. Under this view, some well-known representation results about Gaussian reciprocal processes extend, in a sense, to a ‘non-Gaussian’ case. 相似文献
This article presents new computational techniques for multivariate longitudinal or clustered data with missing values. Current methodology for linear mixed-effects models can accommodate imbalance or missing data in a single response variable, but it cannot handle missing values in multiple responses or additional covariates. Applying a multivariate extension of a popular linear mixed-effects model, we create multiple imputations of missing values for subsequent analyses by a straightforward and effective Markov chain Monte Carlo procedure. We also derive and implement a new EM algorithm for parameter estimation which converges more rapidly than traditional EM algorithms because it does not treat the random effects as “missing data,” but integrates them out of the likelihood function analytically. These techniques are illustrated on models for adolescent alcohol use in a large school-based prevention trial. 相似文献
This is a summary of the author’s Ph.D. thesis, defended on 8 October 2007 at the University of Luxembourg and the Faculté
Polytechnique de Mons, under the joint supervision of Raymond Bisdorff and Marc Pirlot. The thesis is written in English and
is available from the author upon request. The work is situated in the field of multiple criteria decision analysis. It mostly
deals with what we call progressive methods, i.e., iterative procedures presenting partial conclusions to the decision maker that can be refined at further steps of
the analysis. Such progressive methods have been studied in the context of multiattribute value theory and outranking methods.
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We present an algorithm for solving bilevel linear programs that uses simplex pivots on an expanded tableau. The algorithm
uses the relationship between multiple objective linear programs and bilevel linear programs along with results for minimizing
a linear objective over the efficient set for a multiple objective problem. Results in multiple objective programming needed
are presented. We report computational experience demonstrating that this approach is more effective than a standard branch-and-bound
algorithm when the number of leader variables is small. 相似文献
In this paper, we present a supply chain network model with multiple tiers of decision-makers, consisting, respectively, of
manufacturers, distributors, and retailers, who can compete within a tier but may cooperate between tiers. We consider multicriteria
decision-making for both the manufacturers and the distributors whereas the retailers are subject to decision-making under
uncertainty since the demands associated with the product are random. We derive the optimality conditions for the decision-makers,
establish the equilibrium conditions, and derive the variational inequality formulation. We then utilize the variational inequality
formulation to provide both qualitative properties of the equilibrium product shipment, service level, and price pattern and
to propose a computational procedure, along with convergence results. This is the first supply chain network model to capture
both multicriteria decision-making and decision-making under uncertainty in an integrated equilibrium framework. 相似文献
We are interested in the laws of multiple stable stochastic integrals defined
by LePage series representation in references(3,10,11). We continue the study
started in Ref. 3 and give conditions ensuring absolute continuity of joint
laws of stable integrals. To this end, we apply a stratification method on the
Skorohod space on which we first take back the problem. 相似文献
We consider the following problem: given a set of points in the plane, each with a weight, and capacities of the four quadrants, assign each point to one of the quadrants such that the total weight of points assigned to a quadrant does not exceed its capacity, and the total distance is minimized.
This problem is most important in placement of VLSI circuits and is likely to have other applications. It is NP-hard, but the fractional relaxation always has an optimal solution which is “almost” integral. Hence for large instances, it suffices to solve the fractional relaxation. The main result of this paper is a linear-time algorithm for this relaxation. It is based on a structure theorem describing optimal solutions by so-called “American maps” and makes sophisticated use of binary search techniques and weighted median computations.
This algorithm is a main subroutine of a VLSI placement tool that is used for the design of many of the most complex chips. 相似文献