An integrated formulation for selecting the most probable multinomial cell

We refer to the two classical approaches to multinomial selection as the indifference zone approach and the subset selection approach. This paper integrates these two approaches by separating the parameter space into two disjoint subspaces: the preference zone (PZ) and the indifference zone (IZ). In the PZ we insist on selecting the best (most probable) cell for a correct selection (CS ₁) but in the IZ we define any selected subset to be correct (CS ₂) if it contains the best cell. We then propose a single stage procedure R to achieve the selection goals CS ₁ and CS ₂ simultaneously with certain probability requirements. It is shown that both the probability of a correct selection under IZ, P(CS ₂ |PZ), and the probability of a correct selection under IZ, P(CS ₂ |IZ), satisfy some monotonicity properties and the least favorable configuration in PZ and the worst configuration in IZ can be found by these properties.     

Truncated selection procedures for the most probable event and the least probable event

This paper proposes two sequential procedures for selecting respectively the multinomial cell with the largest cell probability and the multinomial cell with the smallest cell probability. The stopping rule for both procedures uses truncation of the procedure studied by Ramey and Alam (1979, Biometrika, 66, 171–173). A property of the least favorable configuration of the proposed procedures is proved, which partially solves a conjecture given in Ramey and Alam (1979). The proposed procedures are compared with other procedures which have been considered in the literature and are found to be better in certain respects.     

Subset selection for the least probable multinomial cell

Summary An inverse sampling procedureR is proposed for selecting a randomsize subset which contains the least probable cell (i.e., the cell with the smallest cell probabilities) from a multinomial distribution withk cells. Type 2-Dirichlet integrals are used (i) to express the probability of a correct selection in terms of integrals with parameters only in the limits of integration, (ii) to prove that the least favorable configuration underR is the so-called slippage configuration withk equal cell probabilities, and (iii) to express exactly the expectation of the total number of observations required and the expectation of the subset size under the procedureR.     

A nonparametric approach for selecting the most reliable population

In this paper, we present a nonparametric method for selecting the most reliable population in reliability life testing. We propose a new statistic,the transitional order statistic, and give a natural selection rule based on this statistic. Under the proposed selection rule, the probability of correct selection is calculated by defining a nonlinear indifference zone under the least favorable configuration. Finally, we present an example to illustrate the selection procedure and validate the results.     

Bias-corrected AIC for selecting variables in multinomial logistic regression models

In this paper, we consider the bias correction of Akaike’s information criterion (AIC) for selecting variables in multinomial logistic regression models. For simplifying a formula of the bias-corrected AIC, we calculate the bias of the AIC to a risk function through the expectations of partial derivatives of the negative log-likelihood function. As a result, we can express the bias correction term of the bias-corrected AIC with only three matrices consisting of the second, third, and fourth derivatives of the negative log-likelihood function. By conducting numerical studies, we verify that the proposed bias-corrected AIC performs better than the crude AIC.     

A sequential discovery sampling procedure

A sequential discovery sampling procedure is to randomly sample and audit one record at a time until either a first record containing a major fault is discovered or a pre-determined maximum number of successive fault-free records has been sampled. If a major fault is discovered, the sampling is terminated and the account is rejected. If no major fault is discovered, the account is accepted and the auditor provides a confidence interval and associated confidence level for the true proportion of records in the account containing major faults. A dynamic programming model incorporating the auditor's degree of belief regarding the existence of records in the account containing major faults, sampling costs and the value of discovery of a major fault is presented. The implied confidence level associated with the optimal maximum sample size of the dynamic programming model provides valuable supplementary information for an auditor in the provision of the confidence level for the required confidence interval.     

Classification methods for random utility models with i.i.d. disturbances under the most probable alternative rule

In this paper random utility maximization based on maximization of correct classification of the choice decisions over a given data set is considered. It is shown that if the disturbance vector in the random utility model is independent and identically distributed, then preference determination based on the most probable alternative reduces to deterministic utility maximization. As a consequence of the above equivalence, the form of the error distribution (normal, Weibull, uniform etc.) plays no role in the determination of the preferred alternative. Parameter estimation under the most probable alternative rule is carried out using two methods. The first is based on the solution of an appropriately defined system of linear inequalities and the second one is based on the function optimization of a newly proposed function, whose optimum is achieved when the number of correctly classified individuals is maximized. The ability to use these algorithms in the framework of pattern recognition and machine learning is pointed out. Simulations and a real case study involving intercity travel behavior are employed to assess the proposed methods.     

Locally most powerful sequential tests for stochastic processes

For a continuous time stochastic process with distribution P_? depending on a one-dimensional parameter ? the problem of sequentially testing ? = 0 against ? > 0 is treated. We assume that the process of likelihood ratios has a certain representation which allows to obtain identities of the Wald type for stopping times. These identities are then used to derive a result on locally most powerful tests for which a problem of optimal stopping is solved.     

A new sequential space-filling sampling strategy for elementary effects-based screening method

To predict or control the response of a complicated numerical model which involves a large number of input variables but is mainly affected by only a part of variables, it is necessary to screening those active variables. This paper proposes a new space-filling sampling strategy, which is used to screening the parameters based on the Morris’ elementary effect method. The beginning points of sampling trajectories are selected by using the maximin principle of Latin Hypercube Sampling method. The remaining points of trajectories are determined by using the one-factor-at-a-time design. Being different from other sampling strategies to determine the sequence of factors randomly in one-factor-at-a-time design, the proposed method formulates the sequence of factors by a deterministic algorithm, which sequentially maximizes the Euclidean distance among sampling trajectories. A new efficient algorithm is proposed to transform the distance maximization problem to a coordinate sorting problem, which saves computational cost much. After the elementary effects are computed using the sampling points, a detailed criterion is presented to select the active factors. Two mathematic examples and an engineering problem are used to validate the proposed sampling method, which demonstrates the priority in computational efficiency, space-filling performance, and screening efficiency.     

Optimal learning for sequential sampling with non-parametric beliefs

We propose a sequential learning policy for ranking and selection problems, where we use a non-parametric procedure for estimating the value of a policy. Our estimation approach aggregates over a set of kernel functions in order to achieve a more consistent estimator. Each element in the kernel estimation set uses a different bandwidth to achieve better aggregation. The final estimate uses a weighting scheme with the inverse mean square errors of the kernel estimators as weights. This weighting scheme is shown to be optimal under independent kernel estimators. For choosing the measurement, we employ the knowledge gradient policy that relies on predictive distributions to calculate the optimal sampling point. Our method allows a setting where the beliefs are expected to be correlated but the correlation structure is unknown beforehand. Moreover, the proposed policy is shown to be asymptotically optimal.     

A new derivation of eigenvalue inequalities for the multinomial distribution

We consider the covariance matrix of the multinomial distribution. We suggest a new derivation of inequalities for the eigenvalues of this matrix using a classical result on the product of two positive semi-definite matrices.     

Cut-sharing across trees and efficient sequential sampling for SDDP with uncertainty in the RHS

Computational Optimization and Applications - Multistage stochastic optimization problems (MSOP) are a commonly used paradigm to model many decision processes in energy and finance. Usually, a set...     

Constructing the best linear combination of diagnostic markers via sequential sampling

We study the best linear combination of markers in terms of the area under the receiver operating characteristic curve, since no single marker is perfect for classification purposes. The sequential fixed-width confidence interval estimate method is applied. We show that the proposed procedure is efficient in terms of the total sample size, with an optimal ratio of cases to controls, and is asymptotically consistent. The performance of our method is illustrated by synthesized data and a real example.     

Estimating the number of zero-one multi-way tables via sequential importance sampling

In 2005, Chen et al. introduced a sequential importance sampling (SIS) procedure to analyze zero-one two-way tables with given fixed marginal sums (row and column sums) via the conditional Poisson (CP) distribution. They showed that compared with Monte Carlo Markov chain (MCMC)-based approaches, their importance sampling method is more efficient in terms of running time and also provides an easy and accurate estimate of the total number of contingency tables with fixed marginal sums. In this paper, we extend their result to zero-one multi-way ( $d$ -way, $d \ge 2$ ) contingency tables under the no $d$ -way interaction model, i.e., with fixed $d-1$ marginal sums. Also, we show by simulations that the SIS procedure with CP distribution to estimate the number of zero-one three-way tables under the no three-way interaction model given marginal sums works very well even with some rejections. We also applied our method to Samson’s monks data set.     

A multinomial tree model for pricing credit default swap options

Among the traded credit derivatives, the market interest in credit default swap options (CDSwaptions) is enormous. We propose a multinomial tree model to price Bermudan CDSwaptions. Our basic rationale is that we distribute the occurring probability for each node in a branch proportional to the probability density function of the assumed (normal) distribution. Through this approach, without the need of solving a large number of equations simultaneously, only the first four moments are required to build an arbitrarily large N-branches tree. We also demonstrate the detailed model implementation procedure including the valuation and the estimation of critical prices through an empirical example in Tucker and Wei (J Fixed Income 15(1):88–95, 2005). Numerical results show that, in the valuation, the proposed multinomial tree model is accurate and can significantly save pricing time under the same degree of accuracy as the binomial tree model. In the estimation of critical prices, the results are less accurate than those in the valuation, but the relative errors are acceptable.     

Finding the most probable nonnegative solutions of systems of linear algebraic equations by the maximum likelihood method

A probabilistic method of regularization is proposed. This method enables obtaining nonnegative solutions to systems of linear algebraic equations. A theorem of existence of the best possible solution is proved. A numerical example application of the method is given.     

A stopping rule for the Robbins-Monro method

A stopping rule for the multidimensional Robbins-Monro stochastic approximation method is developed in this paper. Both moving average and stationary -mixing type of correlated noise processes are treated. Sequentially determined confidence ellipsoids are constructed to fulfill the goal for the determination of the stopping rule. The limit behavior of the algorithm is investigated. It is shown that the stopped Robbins-Monro process is asymptotically normal. Such asymptotic normality is established by means of weak convergence methods.Communicated by Y. C. Ho