Economic designs of single and double screening procedures

Summary Economic designs of single and double screening procedures for improving outgoing product quality based on two screening variables are presented for the case of one-sided specification limit. Two screening variables are observed simultaneously in single screening procedure and are observed sequentially in double screening procedure. It is assumed that the performance variable and the two screening variables are jointly normally distributed. Three quality cost functions — constant, linear, and quadratic — are considered. Cost models are constructed which involve screening inspection cost, and costs of accepted and rejected item. Methods of finding the optimal cutoff values are presented and a numerical example is given.     

Economic design of a two-stage screening procedure with a prescribed outgoing quality

Summary An economic two-stage screening procedure based on screening and performance variables is proposed. A screening variable is used first to decide whether an item should be accepted, rejected, or undecided. The performance variable is then used to classify the undecided items. The two variables are assumed to have a bivariate normal distribution. A cost model is constructed which involves costs due to screening and performance inspections and misclassification errors. Optimal cutoff values on the screening variable minimizing the expected cost are obtained subject to the constraint that the outgoint quality exceeds a prespecified level. Methods of finding the optimal cutoff values are presented for both parameters-known and parameters-unknown cases.     

Economic design of one-sided screening procedures based on a correlated variable with all parameters unknown

Summary Economic design of one-sided screening procedures for improving outgoing product quality based on a screening variable is considered for the case with all parameters unknown. It is assumed that the performance and screening variables are jointly normally distributed and that costs are incurred by screening inspection, acceptance of an imperfect item, and disposition of a rejected one. A method for finding optimal cutoff value based on the predictive distribution is presented.     

Effects of rent dependency on quality of government

This paper introduces quality of government rather than regime type as dependent variable in studies of political effects of natural resources. It consists of two parts. First, it argues for an effect of fiscal dependency of oil and gas rents on quality of government. Second, it finds significant, negative effects of oil and gas rent dependency on three empirical indicators of quality of government—low corruption, bureaucratic quality and legal impartiality—in a sample of 139 states in the period 1984–2006. The results hold for inclusion of control variables such as regime type, income, region and religion.     

Optimal model averaging forecasting in high-dimensional survival analysis

This article considers ultrahigh-dimensional forecasting problems with survival response variables. We propose a two-step model averaging procedure for improving the forecasting accuracy of the true conditional mean of a survival response variable. The first step is to construct a class of candidate models, each with low-dimensional covariates. For this, a feature screening procedure is developed to separate the active and inactive predictors through a marginal Buckley–James index, and to group covariates with a similar index size together to form regression models with survival response variables. The proposed screening method can select active predictors under covariate-dependent censoring, and enjoys sure screening consistency under mild regularity conditions. The second step is to find the optimal model weights for averaging by adapting a delete-one cross-validation criterion, without the standard constraint that the weights sum to one. The theoretical results show that the delete-one cross-validation criterion achieves the lowest possible forecasting loss asymptotically. Numerical studies demonstrate the superior performance of the proposed variable screening and model averaging procedures over existing methods.     

Argumatics: a text analysis procedure for supporting problem formulation

Problem formulation is the most critical phase of the decision making process. This, particularly in the case of high level multi-stakeholder problems, is not trivial, as the problems do not come “neatly packaged”. The procedure presented in this paper aims to support (i) the exploration of the stakeholders' mental constructs, and (ii) the generation, comparison and synthesis of alternative problem formulations. The suggested procedure — while using the decision analytic approach of the traditional text analysis procedures, follows an inverse, bottom-up direction of analysis: first the individual arguments inherent in the text are explored, then taking them as a starting point, the structures behind the arguments are identified.     

Economic screening procedures based on correlated variables

Summary Economic screening procedures for improving outgoing product quality based on screening variables are presented for the cases of one- and two-sided specification limits. It is assumed that the performance and screening variables are jointly normally distributed and that costs are incurred by screening inspection and misclassification errors. When all parameters are known, a closed-form solution is obtained for the case of one-sided specification limit and an approximate closed-form solution is derived for the case of two-sided specification limits. Methods for finding optimal solutions based on normal conditioned ont-distribution are presented for the cases of unknown parameters.     

Estimation and inference in two-stage,semi-parametric models of production processes

Many papers have regressed non-parametric estimates of productive efficiency on environmental variables in two-stage procedures to account for exogenous factors that might affect firms’ performance. None of these have described a coherent data-generating process (DGP). Moreover, conventional approaches to inference employed in these papers are invalid due to complicated, unknown serial correlation among the estimated efficiencies. We first describe a sensible DGP for such models. We propose single and double bootstrap procedures; both permit valid inference, and the double bootstrap procedure improves statistical efficiency in the second-stage regression. We examine the statistical performance of our estimators using Monte Carlo experiments.     

Allocation of public funds to R&;D: a portfolio choice-styled decision model and a biotechnology case study

A decision problem—allocating public research and development (R&D) funding—is faced by a planner who has ambiguous knowledge of welfare effects of the various research areas. We model this as a reverse portfolio choice problem faced by a Bayesian decision-maker. Two elements of the planner’s inferential system are developed: a conditional distribution of welfare ‘returns’ on an allocation, given stated preferences of citizens for the different areas, and a minimum risk criterion for re-allocating these funds, given the performance of a status quo level of funding. A case study of Canadian public research funds expended on various applications of agricultural biotechnology is provided. The decision-making methodology can accommodate a variety of collective expenditure and resource allocation problems.     

The bias of least squares polynomial interpolants

The fitting of a straight line — or more generally of a low degree polynomial — to a point cloud in the plane is a commonly performed statistical technique. This paper discusses the bias of such a procedure and in particular generalizes the well-known remainder formula for polynomial interpolation to the regression setting. Designs minimizing the maximum bias are discussed as well.     

Regulatory Effects and Strategic Global Staffing Profiles: Beyond Cost Concerns in Evaluating Offshore Location Attractiveness

The practice of offshoring—staffing all or part of a business outside the home country—has proliferated to such an extent that the question for most multinational corporations (MNCs) is where, not if, some or all of its labor forces should be located beyond geopolitical borders. It remains an open question, however, where and under what conditions the hoped-for advantages of offshore staffing are best realized. While cost savings continue to play the major role for most companies, both quality and availability of worker skills and administrative and regulatory contexts of labor markets have increasingly influenced global staffing decision processes. This paper has two purposes: to examine the extent to which employment laws and other regulatory factors can impact—beyond cost concerns alone—the decision where to offshore, and to offer a methodology for developing attractiveness profiles that can help governments, service providers, and MNCs evaluate and improve the match between staffing needs and labor market characteristics. By examining financial considerations in conjunction with administrative and regulatory effects, the parties can better manage ongoing expansion of offshore staffing arrangements beyond more established locations such as India, China, and Malaysia. Strategic implications of a trend toward nearshoring—relocating offshore operations closer to or within the home country—are also discussed.     

The impact of state power on economic growth rates in OECD countries

Inspired by public choice theories — in particular the work of Bernholz, Downs, Olson and Tullock — two propositions are advanced. First, the older a democracy is, the slower its growth rates should become. Second, the higher the social security spending is, the slower economic growth rates should become. Pooled regression analysis and the growth experience of 19 OECD nations between 1960 and 1985 allow for an empirical test. Unfortunately, results very much depend on a technical issue, i.e., on the inclusion or exclusion of period and country dummy variables. It is argued that inclusion of the dummies is misleading. If you accept these arguments, then the propositions linking age of democracy or social security transfers on the one hand and lower economic growth rates on the other hand are confirmed.     

A Caution Regarding Rules of Thumb for Variance Inflation Factors

The Variance Inflation Factor (VIF) and tolerance are both widely used measures of the degree of multi-collinearity of the ith independent variable with the other independent variables in a regression model. Unfortunately, several rules of thumb – most commonly the rule of 10 – associated with VIF are regarded by many practitioners as a sign of severe or serious multi-collinearity (this rule appears in both scholarly articles and advanced statistical textbooks). When VIF reaches these threshold values researchers often attempt to reduce the collinearity by eliminating one or more variables from their analysis; using Ridge Regression to analyze their data; or combining two or more independent variables into a single index. These techniques for curing problems associated with multi-collinearity can create problems more serious than those they solve. Because of this, we examine these rules of thumb and find that threshold values of the VIF (and tolerance) need to be evaluated in the context of several other factors that influence the variance of regression coefficients. Values of the VIF of 10, 20, 40, or even higher do not, by themselves, discount the results of regression analyses, call for the elimination of one or more independent variables from the analysis, suggest the use of ridge regression, or require combining of independent variable into a single index.     

Applying lot-by-lot double sampling plan to reform Taiwan National Health Insurance auditing system

This paper seeks to design an elaborate and effective “sampling audit and payment process” for a single-payer system of national health insurance. Furthermore, contrive incentive mechanisms in the “sampling audit and payment process” to make the healthcare providers willing to apply for their medical claim payments straightforwardly. A framework of “medical claim payment auditing by double sampling plan (MCPAD)” procedure based on the lot-by-lot double sampling plan was proposed to curb the growth of medical expenses. The proposed procedure entertains several advantages, including: (1) it meets international standards of sampling plan; (2) it simplifies the auditing process; (3) it reduces sample size and auditing costs; and (4) it encourages healthcare providers using an honest medical claim payment through the incentive mechanisms. This study successfully reduces the sampling cost and effectively audits the claimed medical fees as well as encourages healthcare providers to straightforwardly apply for their medical claim payments. Practically, the proposed MCPAD procedure is also applied to healthcare provider. It is anticipated that the proposed procedure in other nations in the future.     

On the advantages of economically designed the Hotelling’s T2 control chart with variable sample sizes and sampling intervals

Faraz and Parsian (Statistical Paper, 47: 569–593, 2006) have shown that the double warning lines (DWL) scheme detects process shifts more quickly than the other variable ratio sampling schemes such as variable sample sizes (VSS), variable sampling intervals (VSI) and variable sample sizes and sampling intervals (VSSVSI). In this paper, the DWLT²control chart for monitoring the process mean vector is economically designed. The cost model proposed by Costa and Rahim (Journal of Applied Statistics, 28: 875–885, 2001) is used here and is minimized through a genetic algorithm (GA) approach. Then the effects of the model parameters on the chart parameters and resulting operating loss is studied and finally a comparison between all possible variable ratio sampling (VRS) schemes are made to choose the best option economically.     

On economies of scope in communication

A classic puzzle in the economic theory of the firm concerns the fundamental cause of decreasing returns to scale. If a plant producing product quantityX at costC can be replicated as often as desired, then the quantityrX need never cost more thanrC. Traditionally the firm is imagined to take its identity from a fixednon-replicable input, namely a ‘top manager’; as more plants or divisions are added, the communication and computation burden imposed on the top manager (who has information not possessed by the divisions) grows more than proportionately. Decreasing returns are experienced as the top manager hires more variable inputs to cope with the rising burden. Suppose it turns out, however, that when the divisions are assembled, and are given exactly the same totally independent tasks that they fulfilled when they were autonomous, then asaving can be achieved if they adopt a joint procedure for performing those tasks rather than replicating their previous separate procedures. Then the top manager's rising burden must be shown to be particularly onerous—otherwise there may actually beincreasing returns. We show that for a certain model of the information-processing procedure used by the separate divisions and by the firm, there may indeed be such an odd unexpected saving. The saving occurs with respect to the size of the language in which members of each division, or of the firm, communicate with one another, provided that language is finite. If instead the language is a continuum then the saving cannot occur, provided that the procedures used obey suitable ‘smoothness’ conditions. We show that the saving for the finite case can be ruled out in two ways: by requiring the procedures used to obey a regularity condition that is a crude analogue of the smoothness conditions we impose on the continuum procedures, or by insisting that the procedure used be a ‘deterministic’ protocol. Such a protocol prescribes a conversation among the participants, in which a participant has only one choice, whenever that participant has to make an announcement to the others. The results suggest that a variety of information-processing models will have to be studied before the traditional explanation for decreasing returns to scale is understood in a rigorous way.     

Design and investigation of EWMA and double EWMA with quadratic process model in R2R controllers

In the last few years, “Run-to-Run” (R2R) control techniques have been developed and used to control various processes in industries. These techniques combine control engineering and statistical process control techniques. The R2R controller is basically consists of a linear regression model that relates the input variables to the output variables using Exponentially Weighted Moving Average (EWMA) or double EWMA (dEWMA) scheme. In this paper, we have developed a quadratic R2R controller model as an extension of Box, Luceno and Del Castillo models. The validity and performance of the developed models were tested in the magnetic ceramic process. By this study we have found that the quadratic model improves capability of process and applying dEWMA procedure decreases variance of the response variables. The results of the study shows that the quadratic R2R control models outperform linear R2R controls in the certain manufacturing process.     

On the advantages of economically designed variable sample sizes and sampling intervals T2 control chart: double warning lines scheme

Faraz and Parsian (Stat Pap 47:569–593, 2006) have shown that the double warning lines (DWL) scheme detects process shifts more quickly than the other variable ratio sampling schemes such as variable sample sizes (VSS), variable sampling intervals (VSI) and variable sample sizes and sampling intervals (VSSVSI). In this paper, the DWL T² control chart for monitoring the process mean vector is economically designed. The cost model proposed by Costa and Rahim (J Appl Stat 28:875–885, 2001) is used here and is minimized through a genetic algorithm (GA) approach. Then the effects of the model parameters on the chart parameters and resulting operating loss is studied and finally a comparison between all possible variable ratio sampling (VRS) schemes are made to choose the best option economically.     

Using the fuzzy associative memory (FAM) computation to explore the R&;D project performance

This study found out there were nonlinear relationships between the R&D project performance and its determinants—R&D project manager’s skills, quality of project environment, and teamwork effectiveness—by using the Fuzzy Associative Memory (FAM) technique in the IC design industry of Taiwan. The results showed that R&D project manager’s skills and quality of project environment had significantly inverse U-shaped effects on the R&D project performance, while teamwork effectiveness had almost monotonic positive influence upon it. Therefore, there were optimal values for R&D project manager’s skills and quality of project environment for the R&D project performance in the Taiwanese IC design companies, although they can raise teamwork effectiveness as much as possible.     

Interpretable Machine Learning Using Partial Linear Models*

Despite their high predictive performance, random forest and gradient boosting are often considered as black boxes which has raised concerns from practitioners and regulators. As an alternative, we suggest using partial linear models that are inherently interpretable. Specifically, we propose to combine parametric and non-parametric functions to accurately capture linearities and non-linearities prevailing between dependent and explanatory variables, and a variable selection procedure to control for overfitting issues. Estimation relies on a two-step procedure building upon the double residual method. We illustrate the predictive performance and interpretability of our approach on a regression problem.