Hypothesis Testing in Functional Comparative Calibration Models

The purpose of this article is to investigate hypothesis testing in functional comparative calibration models. Wald type statistics are considered which are asymptotically distributed according to the chi-square distribution. The statistics are based on maximum likelihood, corrected score approach, and method of moment estimators of the model parameters, which are shown to be consistent and asymptotically normally distributed. Results of analytical and simulation studies seem to indicate that the Wald statistics based on the method of moment estimators and the corrected score estimators are, as expected, less efficient than the Wald type statistic based on the maximum likelihood estimators for small n. Wald statistic based on moment estimators are simpler to compute than the other Wald statistics tests and their performance improves significantly as n increases. Comparisons with an alternative F statistics proposed in the literature are also reported.     

Fuzzy Autoregressive Rules: Towards Linguistic Time Series Modeling

Fuzzy rule–based models, a key element in soft computing (SC), have arisen as an alternative for time series analysis and modeling. One difference with preexisting models is their interpretability in terms of human language. Their interactions with other components have also contributed to a huge development in their identification and estimation procedures. In this article, we present fuzzy rule–based models, their links with some regime-switching autoregressive models, and how the use of soft computing concepts can help the practitioner to solve and gain a deeper insight into a given problem. An example on a realized volatility series is presented to show the forecasting abilities of a fuzzy rule–based model.     

On limiting behavior for arrays of rowwise negatively orthant dependent random variables

In this paper, the authors study limiting behavior for arrays of rowwise negatively orthant dependent random variables and obtain some new results which extend and improve the corresponding theorems by Hu, Móricz, and Taylor (1989), Taylor, Patterson, and Bozorgnia (2002) and Wu and Zhu (2010).     

Reliability of translated measures assessing dating violence among Mexican adolescents

Research on the prevalence and correlates of dating violence in Mexican teens is challenged by the lack of culturally and linguistically appropriate assessment tools. This study modified, translated, and back-translated the Conflict in Adolescent Dating Relationships Inventory (CADRI; Wolfe et al., 2001) and the Attitudes Towards Dating Violence Scales (Price, Byers, & the Dating Violence Research Team, 1999) for Mexican adolescents. Analyses on 307 adolescents (15-18 years old) from Monterrey and Mexicali, Mexico, revealed that most of the translated CADRI subscales and Attitudes Towards Dating Violence Scales had acceptable internal consistency and test-retest reliability coefficients. The study offers some evidence that the measures may be useful in assessing dating violence in Mexican teens.     

Rating the Rich: An Ordinal Classification to Determine Which Rich Countries are Helping Poorer Ones the Most

When talking about poverty, a lot of energy is expended by academics and sociologists in the identification and classification of the poor. Less attention is paid to classifying the rich. The Center for Global Development created the Commitment to Development Index in 2003, which ranks countries according to their contribution to the reduction of poverty in developing countries. Since its first report, “Ranking the rich, the Index has been quite successful. However, it has also been subject to multiple criticisms. This paper proposes the use of an ordinal classification to rate, not rank, the performance of rich countries. An ordinal classification, where an ordinal scale labels the examples, can help discovering the level of each country’s commitment to development, automatically and independently from others’ performances. It could stimulate both advocacy from civil society and the determination of more coherent public policies in rich countries for poorer ones.The methodology used is Artificial Neural Networks, a common machine learning tool for successfully solving classification problems. Experiments yield robust results, showing better outcomes than other alternative ordinal classifiers, opening the possibility of developing a classification technique which could overcome the limitations of the current ranking technique.     

Incorporating historical two‐arm data in clinical trials with binary outcome: A practical approach

The feasibility of a new clinical trial may be increased by incorporating historical data of previous trials. In the particular case where only data from a single historical trial are available, there exists no clear recommendation in the literature regarding the most favorable approach. A main problem of the incorporation of historical data is the possible inflation of the type I error rate. A way to control this type of error is the so‐called power prior approach. This Bayesian method does not “borrow” the full historical information but uses a parameter 0 ≤ δ ≤ 1 to determine the amount of borrowed data. Based on the methodology of the power prior, we propose a frequentist framework that allows incorporation of historical data from both arms of two‐armed trials with binary outcome, while simultaneously controlling the type I error rate. It is shown that for any specific trial scenario a value δ > 0 can be determined such that the type I error rate falls below the prespecified significance level. The magnitude of this value of δ depends on the characteristics of the data observed in the historical trial. Conditionally on these characteristics, an increase in power as compared to a trial without borrowing may result. Similarly, we propose methods how the required sample size can be reduced. The results are discussed and compared to those obtained in a Bayesian framework. Application is illustrated by a clinical trial example.     

A TECHNIQUE TO MINIMIZE OVERTIME IN THE CAPACITATED MRP PROBLEM

Since the development of early versions of material requirements planning (mrp) systems, it has been known that a weak link in this technique is the failure to consider the available capacity at the time the lot sizes for individual items are calculated. Ignoring the available capacity may result in infeasible production plans, i.e., those that can only be accomplished with the use of overtime. We present a technique to search for feasible production plans by means of minimizing the total overtime. The technique is based on modifying periodic-order-quantity (poq) lot sizes within a tabu search framework. Computational experiments with the largest problem structure reported in the literature show that the procedure is effective in determining lot sizes for individual items that either minimize or eliminate overtime. Additional experiments reveal that, with appropriate calibration of search parameters, the procedure is also able to deal with more general cost functions (e.g., those that include holding and setup costs).     

Accuracy of Numerical Solutions Using the Euler Equation Residuals

This paper is concerned with asymptotic properties on the accuracy of numerical solutions. It is shown that the approximation error of the policy function is of the same order of magnitude as the size of the Euler equation residuals. Moreover, for bounding this approximation error the most relevant parameters are the discount factor and the curvature of the return function. These findings provide theoretical foundations for the construction of tests to assess the performance of alternative computational methods.