On unbiased multinomial estimation

The uniformly minimum variance unbiased estimate of a parametric function and its variance are derived under the inverse multinomial sampling plan of mth order (IMSP(m)), and it is shown that IMSP(m) can be used more advantageously than IMSP(j), j < m when both are applicable.     

On unbiased multinomial estimation

The uniformly minimum variance unbiased estimate of a parametric function and its variance are derived under the inverse multinational sampling plan of mth order (IMSP(m)), and it is shown that IMSP(m) can be used more advantageously than IMSP(j), j<m, when both are applicable.     

Biased versus unbiased estimation

Statisticians have begun to realize that certain deliberately induced biases can dramatically improve estimation properties when there are several parameters to be estimated. This represents a radical departure from the tradition of unbiased estimation which has dominated statistical thinking since the work of Gauss. We briefly describe the new methods and give three examples of their practical application.     

Asymptotic Optimality of Isoperimetric Constants

In this paper, we investigate the existence of L ²(π)-spectral gaps for π-irreducible, positive recurrent Markov chains with a general state space Ω. We obtain necessary and sufficient conditions for the existence of L ²(π)-spectral gaps in terms of a sequence of isoperimetric constants. For reversible Markov chains, it turns out that the spectral gap can be understood in terms of convergence of an induced probability flow to the uniform flow. These results are used to recover classical results concerning uniform ergodicity and the spectral gap property as well as other new results. As an application of our result, we present a rather short proof for the fact that geometric ergodicity implies the spectral gap property. Moreover, the main result of this paper suggests that sharp upper bounds for the spectral gap should be expected when evaluating the isoperimetric flow for certain sets. We provide several examples where the obtained upper bounds are exact.     

Best affine unbiased response decomposition

Given two linear regression models y₁=X₁β₁+u₁ and y₂=X₂β₂+u₂ where the response vectors y₁ and y₂ are unobservable but the sum y=y₁+y₂ is observable, we study the problem of decomposing y into components and , intended to be close to y₁ and y₂, respectively. We develop a theory of best affine unbiased decomposition in this setting. A necessary and sufficient condition for the existence of an affine unbiased decomposition is given. Under this condition, we establish the existence and uniqueness of the best affine unbiased decomposition and provide an expression for it.     

Optimality and efficiency I

Given a set of alternatives, X and a set of value, or utility, functions, V, on X, contemporary work in the Decision Theory field deals with the reduction of X to some acceptable subset Y. This paper deals with the various ways in which this may be done, more specifically with the optimal set, M(X, V), the efficient set E(X, V) and efficient sets ?(X, f), ?(X, f), ?(X, f), when the value of an alternative can be expressed in terms of a multi-objective vector f on X. Relationships between these various sets are explained. The importance of the differences and similarities is seen to depend on the nature of the circumstances faced by the decision analyst at each stage of the evolutionary process of analysis, and upon their use for computational purposes. Some contemporary work (e.g. portfolio analysis) is examined within this framework.     

非光滑多目标规划的最优性

本文给出了一种新的右上导数定义,利用这一右上导数定义了几类广义凸性条件,进而讨论了非光滑多目标规划的最优性．包括Ｆｒｉｔｚ－Ｊｏｈｎ条件和Ｋｕｈｎ－Ｔｕｃｋｅｒ条件．     

Optimality relationships forp-cyclic SOR

Summary The optimality question for blockp-cyclic SOR iterations discussed in Young and Varga is answered under natural conditions on the spectrum of the block Jacobi matrix. In particular, it is shown that repartitioning a blockp-cyclic matrix into a blockq-cyclic form,q, results in asymptotically faster SOR convergence for the same amount of work per iteration. As a consequence block 2-cyclic SOR is optimal under these conditions.Research supported in part by the US Air Force under Grant no. AFOSR-88-0285 and the National Science Foundation under grant no. DMS-85-21154 Present address: Boeing Computer Services, P.O. Box 24346, MS 7L-21, Seattle, WA 98124-0346, USA     

Optimality of generalized Bernstein operators

We show that a certain optimality property of the classical Bernstein operator also holds, when suitably reinterpreted, for generalized Bernstein operators on extended Chebyshev systems.     

Optimality in Social Choice

Marengo and Settepanella (2010) have developed a geometric model of social choice when it takes place among bundles of interdependent elements, showing that by bundling and unbundling the same set of constituent elements an authority has the power of determining the social outcome. In this article, we will tie the model above to tournament theory, solving some of the mathematical problems arising in their work and opening new questions which are interesting from both a mathematical and social choice point of view. In particular, we will introduce the notion of u-local optima and study it from both a theoretical and a numerically probabilistic point of view; we will also describe an algorithm that computes the universal basin of attraction of a social outcome in O(M ³log M) time (where M is the number of social outcomes).     

Optimality of the Lax-Wendroff condition

We show that any spectrally dominant vector norm on the vector space of matrices which is invariant under isometries dominates the numerical radius r(·). Thus, the celebrated Lax-Wendroff stability condition, r(·)?1, defines a maximal isometrically invariant stable set.     

Optimality with telecommunications network

This paper studies a telecommunications company's optimal networkinvestment strategies. The objective of the company is to maximizethe value of the network. According to our numerical exampleswe are not able to consider a single point-to-point connectionseparately from the network, because the network demands andcapacities affect each other. Therefore, the whole network structurehas to be considered in the point-to-point investments.     

On Bayes and unbiased estimators of loss

We consider estimation of loss for generalized Bayes or pseudo-Bayes estimators of a multivariate normal mean vector, θ. In 3 and higher dimensions, the MLEX is UMVUE and minimax but is inadmissible. It is dominated by the James-Stein estimator and by many others. Johnstone (1988, On inadmissibility of some unbiased estimates of loss,Statistical Decision Theory and Related Topics, IV (eds. S. S. Gupta and J. O. Berger), Vol. 1, 361–379, Springer, New York) considered the estimation of loss for the usual estimatorX and the James-Stein estimator. He found improvements over the Stein unbiased estimator of risk. In this paper, for a generalized Bayes point estimator of θ, we compare generalized Bayes estimators to unbiased estimators of loss. We find, somewhat surprisingly, that the unbiased estimator often dominates the corresponding generalized Bayes estimator of loss for priors which give minimax estimators in the original point estimation problem. In particular, we give a class of priors for which the generalized Bayes estimator of θ is admissible and minimax but for which the unbiased estimator of loss dominates the generalized Bayes estimator of loss. We also give a general inadmissibility result for a generalized Bayes estimator of loss. Research supported by NSF Grant DMS-97-04524.     

The limitations of nice mutually unbiased bases

Mutually unbiased bases of a Hilbert space can be constructed by partitioning a unitary error basis. We consider this construction when the unitary error basis is a nice error basis. We show that the number of resulting mutually unbiased bases can be at most one plus the smallest prime power contained in the dimension, and therefore that this construction cannot improve upon previous approaches. We prove this by establishing a correspondence between nice mutually unbiased bases and abelian subgroups of the index group of a nice error basis and then bounding the number of such subgroups. This bound also has implications for the construction of certain combinatorial objects called nets.     

Asymptotic Optimality of Scalar Gersho Quantizers

In his famous paper (Gersho, IEEE Trans. Inf. Theory 25(4):373–380, 1979), Gersho stressed that the codecells of optimal quantizers asymptotically make an equal contribution to the distortion of the quantizer. Motivated by this fact, we investigate in this paper quantizers in the scalar case, where each codecell contributes with exactly the same portion to the quantization error. We show that such quantizers of Gersho type—or Gersho quantizers for short—exist for nonatomic scalar distributions. As a main result, we prove that Gersho quantizers are asymptotically optimal.