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When a parameter of interest is nondifferentiable in the probability, the existing theory of semiparametric efficient estimation is not applicable, as it does not have an influence function. Song (2014) recently developed a local asymptotic minimax estimation theory for a parameter that is a nondifferentiable transform of a regular parameter, where the transform is a composite map of a continuous piecewise linear map with a single kink point and a translation-scale equivariant map. The contribution of this paper is twofold. First, this paper extends the local asymptotic minimax theory to nondifferentiable transforms that are a composite map of a Lipschitz continuous map having a finite set of nondifferentiability points and a translation-scale equivariant map. Second, this paper investigates the discontinuity of the local asymptotic minimax risk in the true probability and shows that the proposed estimator remains to be optimal even when the risk is locally robustified not only over the scores at the true probability, but also over the true probability itself. However, the local robustification does not resolve the issue of discontinuity in the local asymptotic minimax risk.

There is limited literature linking e-fulfillment and product type with postpurchase consumer behavior measures like loyalty, even though there has been a rapid increase in e-tailing. E-fulfillment is defined in literature as a sequence of processes. Each process in this sequence is expected to have a different impact on consumer loyalty across product type. Thus relative importance of e-fulfillment processes leading to consumer loyalty. The purpose of this study is to investigate the impact of e-fulfillment on consumer loyalty across two product types: “standard, regular” and “physical product” (e.g. book, clothing, etc.) and “standard, nonregular” and “physical product” (e.g. computer, refrigerator, etc.) using the peak-end rule theory for an e-tailer.

To know the consumer loyalty for e-fulfillment across the two product types, the partial least square-structural equation modeling approach aided by the SmartPLS 3 tool was used for data analysis as it avoids biases in the parameter estimation in regression analysis. A total of 603 consumer responses through an online and physically administered questionnaire were obtained and were used for the empirical analysis.

Results indicate that for standard, nonregular and physical products, all the e-fulfillment components (customization strategy, website quality, distribution strategy, last mile delivery and return management) positively impact consumer loyalty. Except for the customization strategy, for standard regular and physical product types, all other e-fulfillment components positively impact consumer loyalty.

This study will be helpful to e-tail managers to configure the e-fulfillment components according to product types, thereby increasing consumer loyalty.

While some e-fulfillment components have been linked to consumer loyalty in literature, there is no study establishing linkages between e-fulfillment as a construct and consumer loyalty across product types. This has implications for decision makers in e-tail as the study provides e-fulfillment strategy customization across product types for achieving consumer loyalty in e-tail, a key marketing metric.

Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recently developed in Li, Zeng, and Yu (2014b) to determine the best candidate model. RDIC is a better information criterion than the widely used deviance information criterion (DIC) when latent variables are involved in candidate models. Empirical analysis using US data shows that the optimal model selected by RDIC can be different from that by DIC.

In this paper, we study partial identification of the distribution of treatment effects of a binary treatment for ideal randomized experiments, ideal randomized experiments with a known value of a dependence measure, and for data satisfying the selection-on-observables assumption, respectively. For ideal randomized experiments, (i) we propose nonparametric estimators of the sharp bounds on the distribution of treatment effects and construct asymptotically valid confidence sets for the distribution of treatment effects; (ii) we propose bias-corrected estimators of the sharp bounds on the distribution of treatment effects; and (iii) we investigate finite sample performances of the proposed confidence sets and the bias-corrected estimators via simulation.

Planning an accelerated life test (ALT) for a product is an important task for reliability practitioners. Traditional methods to create an optimal design of an ALT are often computationally burdensome and numerically difficult. In this paper, the authors introduce a practical method to find an optimal design of experiments for ALTs by using simulation and empirical model building.

Instead of developing the Fisher information matrix-based objective function and analytic optimization, the authors suggest “experiments for experiments” approach to create optimal planning. The authors generate simulated data to evaluate the quantity of interest, e.g. 10th percentile of failure time and apply the response surface methodology (RSM) to find an optimal solution with respect to the design parameters, e.g. test conditions and test unit allocations. The authors illustrate their approach applied to the thermal ALT with right censoring and lognormal failure time distribution.

The design found by the proposed approach shows substantially improved statistical performance in terms of the standard error of estimates of 10th percentile of failure time. In addition, the approach provides useful insights about the sensitivity of each decision variable to the objective function.

More comprehensive experiments might be needed to test its scalability of the method.

This method is practically useful to find a reasonably efficient optimal ALT design. It can be applied to any quantities of interest and objective functions as long as those quantities can be computed from a set of simulated datasets.

This is a novel approach to create an optimal ALT design by using RSM and simulated data.

In their now classic book What Do Unions Do? Freeman and Medoff (1984) open their discussion as follows (p. 3): “Trade unions are the principal institution of workers in modern capitalistic societies. For over 200 years, since the days of Adam Smith, economists and other social scientists, labor unionists, and businessmen and women have debated the social effects of unionism. Despite the long debate, however, no agreed-upon answer has emerged to the question: What do unions do?” In the remainder of the book, the authors provide a coherent answer to this question and, as a result, What Do Unions Do? has become firmly established as a cornerstone for the economic analysis of labor unions. However, since the first publication of the book, developments have emerged that are inconsistent with Freeman and Medoff's predictions regarding the effectiveness of unions. One such development, for example, is the precipitous decline in union density in many countries around the world; another is the growing importance of, and interest in, alternative forms of employee representation.

Presents an implementation of the algebraic multigrid method. It can work in two ways: as pure multigrid method and as a pre‐conditioner for the conjugate gradient method. Shows applications of the iterative solvers for problems in linear and non‐linear elasticity. Shows the range of possible applications with different examples with regular and non‐regular meshes and three‐dimensional problems.