Economic designs of two-sided single and double screening procedures

Economic designs of single and double screening procedures for improving outgoing product quality based on two screening variables are presented for the case of two-sided specification limits. Two screening variables are observed simultaneously in the single screening procedure. In the double screening procedure, one variable is used first to make one of three decisions — accept, reject, or undecided — and after the first screening, the second variable is employed to screen the undecided items. It is assumed that the performance and the two screening variables are jointly normally distributed, and the deviation of the performance variable from the ‘ideal’ value causes dissatisfication to the consumers. Two quality cost functions — constant 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 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 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.     

Non-linearity of scorecard log-odds

The use of linear and log-linear models for scorecard construction is nearly universal. In this paper we address the question of non-linearity in the distribution of a scorecard’s inferred log-odds to score relationship. Linear scorecards are excellent and robust ranking tools, but the inferred default probabilities are increasingly used in day-to-day business operations — within account-level strategies, for cutoff setting, and for capital allocation. All of these uses are dependent upon the accurate estimation of the probability of default, which is a quality independent of a model’s ranking performance.In this paper we illustrate this phenomenon of a misaligned log-odds relationship within a model built using a method which is standard in retail banking. The sequential elimination of correlated variable bins results in an improved estimation, at the cost of the model’s ranking performance. A post-hoc quadratic transformation of the scores improves the model fit without affecting the ranking performance, but may prove difficult to apply in current operational systems.     

Using frailty models to account for heterogeneity in multistage manufacturing and service processes

Statistical process control has been widely applied to manufacturing and service operations with the aim of monitoring and improving the reliability of products. The existing monitoring procedures were introduced following the assumption that a single-stage process with independent quality characteristic is under consideration. However, in multistage processes with dependent variables, quality characteristics of interest should be optimally monitored only after they have been adjusted for the effect of influential covariates. In general, it is impossible to include all relevant covariates because measuring such values entails great financial costs. The neglect of such covariates results in having unobserved heterogeneity which dampens the detection ability of the monitoring procedure. The more complicated picture arises when the values corresponding to the reliability-related quality variable are censored due to the time and cost constraints. Thus, to deal with the effect of observed and unobserved covariates together with the censoring issue, the frailty and the proportional hazard models are used and some model-based monitoring schemes are devised. The surveillance procedures are proposed in both the presence and absence of a censoring mechanism. The performance analysis shows that the monitoring procedure based on the cumulative sum chart is superior in detecting shifts while the exponentially weighted moving average chart is effective in some cases.     

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.     

Statistical inference on restricted partial linear regression models with partial distortion measurement errors

We consider the estimation and hypothesis testing problems for the partial linear regression models when some variables are distorted with errors by some unknown functions of commonly observable confounding variable. The proposed estimation procedure is designed to accommodate undistorted as well as distorted variables. To test a hypothesis on the parametric components, a restricted least squares estimator is proposed under the null hypothesis. Asymptotic properties for the estimators are established. A test statistic based on the difference between the residual sums of squares under the null and alternative hypotheses is proposed, and we also obtain the asymptotic properties of the test statistic. A wild bootstrap procedure is proposed to calculate critical values. Simulation studies are conducted to demonstrate the performance of the proposed procedure, and a real example is analyzed for an illustration.     

The standardized effect of a compound of dummy variables or polynomial terms

A method is proposed to obtain standardized regression coefficients for composite variables made up of dummy variables or polynomial terms. The method to be described enables the researcher to compare the effect of the composite variable with the effects of other predictor variables. Forming a composite variable is particularly useful in polynomial regression where individual regression coefficients are hard to interpret. A second application is assessing the impact of a compound of dummy variables. An empirical example dealing with the curvilinear relationship between church involvement and prejudice is used to illustrate the approach.     

Local structural quantile effects in a model with a nonseparable control variable

I consider a semiparametric version of the nonseparable triangular model of Chesher [Chesher, A., 2003. Identification in nonseparable models. Econometrica 71, 1405–1441]. The proposed model is linear in coefficients, where the coefficients are unknown functions of unobserved latent variables. Using a control variable idea and quantile regression methods, I propose a simple two-step estimator for the coefficients evaluated at particular values of the latent variables. Under the condition that the instruments are locally relevant (i.e. they affect a particular conditional quantile of interest of the endogenous variable) I establish consistency and asymptotic normality. Simulation experiments confirm the theoretical results.     

Evaluating the global efficiency of teachers through a multi-criteria approach

Improving the quality of education is one of the main objectives for governments, given that it plays an essential role in providing well-being to the population. In this regard, the efficiency of teachers may be a key issue to improve the students' academic performance and learning achievement. Specifically, in this paper, we analyse the efficiency of teachers from a multi-criteria optimization perspective. The purpose is to detect the most efficient teachers to gain knowledge about the characteristics of their teaching context, which contribute to increase the education performance of the students. To this aim, data from TIMSS and PIRLS 2011 for fourth-grade reading, mathematics and science teachers in Spain have been used. Three synthetic indexes gathering these data are calculated: a weak index allowing full compensation among the variables, a strong index informing about the worst variable value (no compensation scheme) and a synthetic index representing a different degree of compensation of the two formers. These indexes are built using aspiration and reservation levels for each variable. This allows us to include desirable ranges of values to be achieved by the variables in the study of the teachers' efficiency. With this, it is possible to know if the teachers are performing better, within or worse than the desired limits, and to rank the teachers according to their efficiency. The analysis carried out has contributed to have a better understanding of the policies to be followed in order to promote efficiency in teaching.     

A multiple group analysis of the training transfer model: exploring the differences between high and low performers in a Korean insurance company

This study suggests an integrative transfer of training model and examines the influence of pre-training performance on the relationship between work environment variables and transfer-related variables. Structural equation modeling was conducted with data collected from 365 Korean employees of a large general insurance firm. They were all participants in a leadership development program. The results of multi-group analyses revealed that the influence of work environment variables on transfer-related variables was moderated by employees' pre-training performance. The findings of the study imply that a more elaborate training transfer model that considers performance not just as an outcome variable, but also as an antecedent variable is required. Also, it is proposed that different strategies should be applied to promote transfer of training based on the employee's level of pre-training performance.     

Target Costing Performance Based on Alternative Participation and Evaluation Methods: A Laboratory Experiment

This paper deals with the motivational impacts of alternative participation and performance-evaluation methods on the cost- reduction performance of product designers in the product development process. Alternative participation methods for establishing the target cost consist of the participative and the nonparticipative approaches. Also, the alternative performance-evaluation methods include evaluations based on only the controllable item measure and on both controllable and uncontrollable items measures. To test the hypothesis which are proposed with respect to the above impacts, a laboratory experiment was conducted on 120 subjects. When participation and performance evaluation factors are considered separately, the cost-reduction performance of product designers is improved if they can participate in the target-setting process and are evaluated by their controllable item information. In investigating their joint influence, it is found that the combination of the participative method and controllable item and nonparticipative and uncontrollable item information have improved cost-reduction performance. Among all the independent variables, controllable item information is the most dominant variable as it has the strongest influence on cost reduction. © 1997 John Wiley & Sons, Ltd.     

The differential impact of product complexity,inventory level,and configuration capacity on unit and order fill rate performance

This paper examines the simultaneous impact of configuration capacity, inventory level, and complexity on service performance as measured by unit and order fill rates in a configure-to-order environment. Demand skew is treated as a control variable. A simulation model based on data from a leading electronics manufacturer is used to test the hypotheses and identify the impact. Results suggest that there are differential direct and interactive effects of examined variables on unit and order fill rates.     

Een simpele grafische iteratiemethode voor het schatten van een betrekkingtussen kwantitatievestochastische variabelen.

A simple graphical iteration procedure for estimating a relation between quantitative stochastic variables.
For the estimation of a relation which will predict values of one stochastic variable from known values of a number of other ones, a simple graphical iteration procedure, applicable under rather general conditions, is demonstrated by means of an example. The method amounts to plotting the values of the dependent variable against one (or one pair) of the initial variables, after correction for the influences of the other initial variables as far as these have already been estimated. An approximation (or improved approximation) for the influence of the variable considered can then be made.     

Estimating the LQAC model with I(2) variables

This paper derives a method for estimating and testing the Linear Quadratic Adjustment Cost (LQAC) model when the target variable and some of the forcing variables follow I(2) processes. Based on a forward-looking error-correction formulation of the model it is shown how to obtain strongly consistent estimates of the structural parameters from both a linear and a non-linear cointegrating regression where first-differences of the I(2) variables are included as regressors (multicointegration). Further, based on the estimated parameter values, it is shown how to test and evaluate the LQAC model using a VAR approach. A simple easy interpretable metric for measuring the model fit is suggested. In an empirical application using UK money demand data, the non-linear multicointegrating regression delivers an economically plausible estimate of the adjustment cost parameter. However, the restrictions implied by the exact LQAC model under rational expectations are strongly rejected and the metric for model fit indicates a substantial noise component in the model. Copyright © 1999 John Wiley & Sons, Ltd.     

Econometric Analysisof Collusive Behaviorin a Soft-Drink Market

This paper proposes an empirical methodology for studying various (implicit or explicit) collusive behaviors on two strategic variables, which are price and advertising, in a differentiated market dominated by a duopoly. In addition to Nash or Stackelberg behaviors, we consider collusion on both variables, collusion on one variable and competition on the other, etc. Using data on the Coca-Cola and Pepsi-Cola markets from 1968 to 1986, full information maximum likelihood estimation of cost and demand functions are obtained allowing for various collusive behaviors. The collusive hypothesis is not rejected, and the best form of collusive behavior is selected via nonnested testing procedures. Using the best model, Lerner indices are computed for both duopolists to provide summary measures of market power. Finally, our approach is contrasted with the conjectural variation approach and is shown to give superior results.     

RIDIT ANALYSIS, A REVIEW

In ridit analysis scale values are assigned to the categories of an ordinal response variable, the ridits. Ridit analysis is described as a technique to compare groups of observations and to measure the association between ordinal and nominal variables by mean ridits. By their definition mean ridits are closely related to distribution-free methods. Mean ridits are also used as a tool to test the main and/or interaction effects of factors on an ordinal response variable.     

Modeling Criminal Activity in an Area in Economic Decline: Local Economic Conditions Are a Major Factor in Local Property Crimes

A bstract . A multi-factor model includes economic, apprehension, seasonal a n d plant closing variables as the explanatory regressors and crimes against property as the dependent variable. Different lag structures were used on the explanatory variables such as an Almon distributed lag of a second degree polynominal nature and the lagging of the dependent variable by one quarter so that the model would more closely approximate the environment being considered. The results suggest a definite seasonal pattern in crimes against property, and the economic variables measuring local, not national, conditions, appear to be more significant regressors than any other explanatory variables.     

Multi-modal,multi-output,multiregional variable input-output model

A multi-modal, multi-output, multiregional variable input-output (MMMVIO) model is introduced to evaluate the economic impact of a transportation system. The MMVIO model differs from the conventional input-output models by being price and cost sensitive. The regional technical coefficients, trade coefficients, modal choice of shipment, input mix and output composition are determined by the price and cost variables, a property not shared by the conventional input-output models.The transportation system reduces shipping cost of delivering commodities between regions, thereby stimulating economy of trading regions. The MMMVIO model captures the development impact incident to the transportation system.     

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.