Robust estimation methods for exponential data: a monte-carlo comparison

We compare the performance of seven robust estimators for the parameter of an exponential distribution. These include the debiased median and two optimally-weighted one-sided trimmed means. We also introduce four new estimators: the Transform, Bayes, Scaled and Bicube estimators. We make the Monte Carlo comparisons for three sample sizes and six situations. We evaluate the comparisons in terms of a new performance measure, Mean Absolute Differential Error (MADE), and a premium/protection interpretation of MADE. We organize the comparisons to enhance statistical power by making maximal use of common random deviates. The Transform estimator provides the best performance as judged by MADE. The singly-trimmed mean and Transform method define the efficient frontier of premium/protection.     

Price Manipulation and Quasi‐Arbitrage

In an environment where trading volume affects security prices and where prices are uncertain when trades are submitted, quasi‐arbitrage is the availability of a series of trades that generate infinite expected profits with an infinite Sharpe ratio. We show that when the price impact of trades is permanent and time‐independent, only linear price‐impact functions rule out quasi‐arbitrage and thus support viable market prices. When trades have also a temporary price impact, only the permanent price impact must be linear while the temporary one can be of a more general form. We also extend the analysis to a time‐dependent framework.     

Disentangling combinations and contingencies of generic strategies: A set-theoretic configurational approach

Porter's framework of generic strategies conceptualizes key elements of firms' strategic positions in their industry. Research on these generic strategies has been challenged by the complexity of capturing relations between generic strategies (i.e., cost leadership, differentiation, and focus) and their interdependencies with other strategic commitments for performance outcomes. We address these challenges by utilizing fuzzy set Qualitative Comparative Analysis (fsQCA) to explore the causal complexity underlying the links between generic strategies and firm performance. Specifically, we study combinations of firms' generic strategies and other strategy attributes as paths to high or low performance in the U.S. and Canadian airline industry. We find six equifinal configurations that are consistently linked to high performance and nine configurations that are consistently linked to the absence of high performance. Our findings shed light on the contingencies underlying performance consequences of generic strategies. Consequently they redirect theoretical debates on the links between generic strategies and firm performance as well as on the superiority of pure versus hybrid generic strategies towards relations of set-theoretic of necessity and sufficiency. Specifically, we show that having a generic strategy advantage is not sufficient for high performance but that it is a vital ingredient in recipes for success. Similarly, our findings suggest that a generic strategy disadvantage is not sufficient for low performance, but is a key ingredient in all paths to poor performance. We conclude with a discussion of our study's contributions to the literature, suggestions for future research, and implications for managerial practice.     

Exact Bootstrap Variances of the Area Under ROC Curve

The area under the Receiver Operating Characteristic (ROC) curve (AUC) and related summary indices are widely used for assessment of accuracy of an individual and comparison of performances of several diagnostic systems in many areas including studies of human perception, decision making, and the regulatory approval process for new diagnostic technologies. Many investigators have suggested implementing the bootstrap approach to estimate variability of AUC-based indices. Corresponding bootstrap quantities are typically estimated by sampling a bootstrap distribution. Such a process, frequently termed Monte Carlo bootstrap, is often computationally burdensome and imposes an additional sampling error on the resulting estimates. In this article, we demonstrate that the exact or ideal (sampling error free) bootstrap variances of the nonparametric estimator of AUC can be computed directly, i.e., avoiding resampling of the original data, and we develop easy-to-use formulas to compute them. We derive the formulas for the variances of the AUC corresponding to a single given or random reader, and to the average over several given or randomly selected readers. The derived formulas provide an algorithm for computing the ideal bootstrap variances exactly and hence improve many bootstrap methods proposed earlier for analyzing AUCs by eliminating the sampling error and sometimes burdensome computations associated with a Monte Carlo (MC) approximation. In addition, the availability of closed-form solutions provides the potential for an analytical assessment of the properties of bootstrap variance estimators. Applications of the proposed method are shown on two experimentally ascertained datasets that illustrate settings commonly encountered in diagnostic imaging. In the context of the two examples we also demonstrate the magnitude of the effect of the sampling error of the MC estimators on the resulting inferences.     

An integrated multi-objective decision-making process for multi-period lot-sizing with supplier selection

Supplier selection is a multi-criteria problem which includes both tangible and intangible factors. In these problems if suppliers have capacity or other different constraints two problems will exist: which suppliers are the best and how much should be purchased from each selected supplier? In this paper an integrated approach of analytic network process (ANP) and multi-objective mixed integer linear programming (MOMILP) is proposed. This integrated approach considers both tangible and intangible factors in choosing the best suppliers and defines the optimum quantities among selected suppliers to maximize the total value of purchasing (TVP), and to minimize the total cost and total defect rate and to balance the total cost among periods. The priorities are calculated for each supplier by using ANP. Four different plastic molding firms working with a refrigerator plant are evaluated according to 14 criteria that are involved in the four clusters: benefits, opportunities, costs and risks (BOCR). The priorities of suppliers will also be used as the parameters of the first objective function. This multi-objective and multi-period real-life problem is solved by using previous techniques and a reservation level driven Tchebycheff procedure (RLTP). Finally the most preferred nondominated solutions are determined by considering the decision maker's (DM's) preferences and the results obtained by these techniques are compared.     

Controlling for Response Biases Clarifies Sex and Age Differences in Facial Affect Recognition

Sex, age and education differences in facial affect recognition were assessed within a large sample (n = 7,320). Results indicate superior performance by females and younger individuals in the correct identification of facial emotion, with the largest advantage for low intensity expressions. Though there were no demographic differences for identification accuracy on neutral faces, controlling for response biases by males and older individuals to label faces as neutral revealed sex and age differences for these items as well. This finding suggests that inferior facial affect recognition performance by males and older individuals may be driven primarily by instances in which they fail to detect the presence of emotion in facial expressions. Older individuals also demonstrated a greater tendency to label faces with negative emotion choices, while females exhibited a response bias for sad and fear. These response biases have implications for understanding demographic differences in facial affect recognition.     

Arguing for a Generous Identity

A generous person is one who freely gives assistance to others. In this article, we provide evidence in support of the existence of a generous identity. To do this, we first outline ways in which generosity relates to other constructs such as altruism, helping behavior, volunteerism, and philanthropy and offer explanations as to why it is important to study generous identity both alone and in tandem with these related constructs. Next, we briefly discuss how generosity can be conceptualized as a person identity. We then recommend best practices for developing and implementing a measure for generous identity. Lastly, we discuss the implications of a generous identity and ways in which its salience can play a role in different contexts.     

A two-agent single machine scheduling problem with due-window assignment and a common flow-allowance

We study a single-machine scheduling model combining two competing agents and due-date assignment. The basic setting involves two agents who need to process their own sets of jobs, and compete on the use of a common processor. Our goal is to find the joint schedule that minimizes the value of the objective function of one agent, subject to an upper bound on the value of the objective function of the second agent. The scheduling measure considered in this paper is minimum total (earliness, tardiness and due-date) cost, based on common flow allowance, i.e., due-dates are defined as linear functions of the job processing times. We introduce a simple polynomial time solution for this problem (linear in the number of jobs), as well as to its extension to a multi-agent setting. We further extend the model to that of a due-window assignment based on common flow allowance.