Model-Based Stratification in Inventory Cost Estimation

This article describes a method for developing an efficiently stratified sampling design for a generalized difference estimator, using a superpopulation model. The effectiveness of model-based stratification is compared to several conventional designs, using a data base from a complete audit of an inventory of 8,069 items. In this application, model-based designs reduce the required sample size from 9% to 40%, compared to the conventional designs.     

On the Functional Approach to Bayesian Efficient Designs for Nonlinear Regression Models

The present article is devoted to an extension of the functional approach elaborated in the book Melas (2006 Melas , V. B. ( 2006 ). Functional Approach to Optimal Experimental Design . Lecture Notes in Statistics , Vol. 184. Heidelberg : Springer . [Google Scholar]) for studying optimal designs in linear and nonlinear regression models. Here we consider Bayesian efficient designs for nonlinear models under the standard assumptions on the observational errors. Sufficient conditions for uniqueness of locally optimal and Bayesian efficient designs for common optimality criteria are given. L-efficient Bayesian designs are constructed and investigated for a special nonlinear regression model of a rational form as an illustration of our main results. This model is interesting in both a practical and a theoretical sense.     

Block Design with Nested Rows and Columns for Research on Food Acceptability Limitation

Block designs with nested rows and columns were introduced in Srivastava (1978 Srivastava , J. N. ( 1978 ). Statistical design of agricultural experiments . J. Ind. Soc. Agric. Statist. 30 : 1 – 10 . [Google Scholar]) and more specifically defined in Singh and Dey (1979 Singh , M. , Dey , A. ( 1979 ). Block designs with nested rows and columns . Biometrika 66 ( 2 ): 321 – 326 .[Crossref], [Web of Science ®] , [Google Scholar]). We present properties of some partially balanced block designs with nested rows and columns. The designs are considered for near-factorial experiments, when there are a levels of experimental factor A and b levels of experimental factor B and there is one control treatment added. In this article we extend the theory of block designs with nested rows and columns described by \L acka and Koz?owska (2009 ?acka , A. , Koz?owska , M. ( 2009 ). Planning of factorial experiments in a block design with nested rows and columns for environmental research . Environmetrics 20 ( 6 ): 730 – 742 .[Crossref], [Web of Science ®] , [Google Scholar]) and Koz?owska et al. (2011 Koz?owska , M. , ?acka , A. , Krawczyk , R. , Koz?owski , R. J. ( 2011 ). Some block designs with nested rows and columns for research on pesticide dose limitation . Environmetrics 22 ( 6 ): 781 – 788 .[Crossref], [Web of Science ®] , [Google Scholar]). We carry out our consideration by the derived mixed linear model resulting from randomization of blocks, rows and columns. For this model of observations, some properties of estimation of treatment contrasts are discussed. We calculate the efficiency factors of estimation of treatment contrasts. We formulate theorems for some partially balanced block designs with nested rows and columns. Plant protection experiment on limitation of food acceptability for T. urticae is given to show how the obtained results can be applied.     

Pricing of American options in discrete time using least squares estimates with complexity penalties

Pricing of American options in discrete time is considered, where the option is allowed to be based on several underlying stocks. It is assumed that the price processes of the underlying stocks are given by Markov processes. We use the Monte Carlo approach to generate artificial sample paths of these price processes, and then we use nonparametric regression estimates to estimate from this data so-called continuation values, which are defined as mean values of the American option for given values of the underlying stocks at time t subject to the constraint that the option is not exercised at time t. As nonparametric regression estimates we use least squares estimates with complexity penalties, which include as special cases least squares spline estimates, least squares neural networks, smoothing splines and orthogonal series estimates. General results concerning rate of convergence are presented and applied to derive results for the special cases mentioned above. Furthermore the pricing of American options is illustrated by simulated data.     

Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective

In this article, we study a nonparametric approach regarding a general nonlinear reduced form equation to achieve a better approximation of the optimal instrument. Accordingly, we propose the nonparametric additive instrumental variable estimator (NAIVE) with the adaptive group Lasso. We theoretically demonstrate that the proposed estimator is root-n consistent and asymptotically normal. The adaptive group Lasso helps us select the valid instruments while the dimensionality of potential instrumental variables is allowed to be greater than the sample size. In practice, the degree and knots of B-spline series are selected by minimizing the BIC or EBIC criteria for each nonparametric additive component in the reduced form equation. In Monte Carlo simulations, we show that the NAIVE has the same performance as the linear instrumental variable (IV) estimator for the truly linear reduced form equation. On the other hand, the NAIVE performs much better in terms of bias and mean squared errors compared to other alternative estimators under the high-dimensional nonlinear reduced form equation. We further illustrate our method in an empirical study of international trade and growth. Our findings provide a stronger evidence that international trade has a significant positive effect on economic growth.     

Sharpley-Snow定理证明的注记

本文给出了对策论中著名的Sharpley-Snow定理必要性的另一证明,明确了其中非奇异矩阵B的取法,且证明方法不同于单纯形证法。     

Sequential sampling without recall from a dirichlet process

The problem of sequential sampling without recall from a Dirichlet process is studied. An individual observes a sequential sample p₁, p₂, … . At each stage the observer can either accept the current value p_i or continue sampling. The total cost to the observer is the current value plus a cost for each observation taken after the first. A concrete context for the problem is provided by a shopper who must buy a particular item and can elicit price quotations sequentially but must pay for each quotation. Qualitative properties of optimal search rules are derived which establish that the problem is well behaved. In particular, the reservation-price property is shown to hold. The results extend those in Christensen (1986).     

Fitting a multiple regression function

Consider the p-dimensional unit cube [0,1]^p, p≥1. Partition [0, 1]^p into n regions, R_1,n,…,R_n,n such that the volume Δ(R_j,n) is of order n^?1,j=1,…,n. Select and fix a point in each of these regions so that we have x⁽ⁿ⁾₁,…,x⁽ⁿ⁾_n. Suppose that associated with the j-th predictor vector x⁽ⁿ⁾_j there is an observable variable Y⁽ⁿ⁾_j, j=1,…,n, satisfying the multiple regression model $\text{Y}{}^{\mathrm{(n)}}{}_{\mathrm{j}}\text{=g(}\text{x}{}^{\mathrm{(n)}}{}_{\mathrm{j}}\text{)+e}{}^{\mathrm{(n)}}{}_{\mathrm{j}}$, where g is an unknown function defined on [0, 1]^pand {e⁽ⁿ⁾_j} are independent identically distributed random variables with Ee⁽ⁿ⁾₁=0 and Var e⁽ⁿ⁾₁=σ²<∞. This paper proposes as an estimator of g(x), where k(u) is a known p-dimensional bounded density and {a_n} is a sequence of reals converging to 0 asn→∞. Weak and strong consistency of g_n(x) and rates of convergence are obtained. Asymptoticnormality of the estimator is established. Also proposed is $\text{σ}{}^2{}_{\mathrm{n}}\text{=n}{}^{\mathrm{?1}}\text{Σ}{}^{\mathrm{n}}{}_{\mathrm{j=1}}\text{(Y}{}^{\mathrm{(n)}}{}_{\mathrm{j}}\text{?g}{}_{\mathrm{n}}\text{(}\text{x}{}^{\mathrm{(n)}}{}_{\mathrm{j}}\text{))}{}^2$ as a consistent estimate of σ².     

Numerical estimation of a probability measure

An iterative solution to the problem of maximizing a concave functional ø defined on the set of all probability measures on a topological space is considered. Convergence of this procedure and a rapidly converging algorithm are studied. Computational aspects of this algorithm along with the ones developed earlier by Wynn, Fedorov, Atwood, Wu and others are provided. Examples discussed are taken from the area of mixture likehoods and optimal experimental design.