A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

Strategic planning in smaller rapid growth companies

The research reported in this article is based on a study of the strategic planning practices of the 500 fastest growing privately held smaller companies in the United States ranked according to percentage of sales increases from 1978 to 1982.^p1 Based on the information provided, a clear picture emerges regarding the actual role of strategic planning in rapid growth companies.     

Parent‐Adolescent Communication About Sex: Retrospective Reports by Latino College Students

Latina female (n= 97) and Latino male (n= 69) college students (M age = 21.4 years) completed self‐report surveys regarding family of origin experiences, including sexual communication with parents while growing up. Latino parents of this comparatively highly educated sample tended to use direct rather than indirect strategies for communicating about sexuality with their children. Young women reported higher levels of sexual communication with mothers while growing up than did young men, and respondents reported less communication with fathers than mothers. Among young women, sexual communication with mother was positively associated with non‐Mexican origin and negatively associated with having older brothers living at home. In contrast, maternal education was positively associated with mother‐son communication about sex. Paternal education and the absence of older brothers positively predicted communication with both sons and daughters. The analyses provide novel information regarding sexual communication in Latino families and suggest directions for future research.     

Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances

We study the properties of the quasi-maximum likelihood estimator (QMLE) and related test statistics in dynamic models that jointly parameterize conditional means and conditional covariances, when a normal log-likelihood os maximized but the assumption of normality is violated. Because the score of the normal log-likelihood has the martingale difference property when the forst two conditional moments are correctly specified, the QMLE is generally Consistent and has a limiting normal destribution. We provide easily computable formulas for asymptotic standard errors that are valid under nonnormality. Further, we show how robust LM tests for the adequacy of the jointly parameterized mean and variance can be computed from simple auxiliary regressions. An appealing feature of these robyst inference procedures is that only first derivatives of the conditional mean and variance functions are needed. A monte Carlo study indicates that the asymptotic results carry over to finite samples. Estimation of several AR and AR-GARCH time series models reveals that in most sotuations the robust test statistics compare favorably to the two standard (nonrobust) formulations of the Wald and IM tests. Also, for the GARCH models and the sample sizes analyzed here, the bias in the QMLE appears to be relatively small. An empirical application to stock return volatility illustrates the potential imprtance of computing robust statistics in practice.     

Flipping and ex post aggregation

We show that Bayesian ex post aggregation is unstable with respect to refinements. Suppose a group of Bayesians use ex post aggregation. Since it is a joint problem, each agent's problem is captured by the same model, but probabilities and utilities may vary. If they analyze the same situation in more detail, their refined analysis should preserve their preferences among acts. However, ex post aggregation could bring about a preference reversal on the group level. Ex post aggregation thus depends on how much information is used and may keep oscillating (“flipping”) as one keeps adding more information. Received: 16 April 2002/Accepted: 27 May 2002     

Farmers,the Practice of Farming and the Future of Agroforestry: An Application of Bourdieu's Concepts of Field and Habitus

Abstract Agroforestry, the practice of raising crops and trees together in ways that are mutually beneficial, provides farmers with an alternative to more conventional farming practices. In this paper, we apply Bourdieu's concepts of “field” and “habitus” in an attempt to better understand the practice of farming and the role that agroforestry may have in farming systems. Analysis is based on qualitative and quantitative interviews of farmers and other key informants in two regions, Fox‐Wyaconda Watershed in northeast Missouri and Scott County in southeast Missouri. Within the field of farming, farmers emphasized the importance of economic, family and rental relations. Important habitus considerations include different interpretations of what constitutes farming and what constitutes forestry. Based on Bourdieu's theoretical framework, we offer three alternative paths of social change that may lead to more widespread utilization of agroforestry.     

Modelling data from single-photon emission computerized tomography

The completely random character of radioactive disintegration provides the basis of a strong justification for a Poisson linear model for single-photon emission computed tomography data, which can be used to produce reconstructions of isotope densities, whether by maximum likelihood or Bayesian methods. However, such a model requires the construction of a matrix of weights, which represent the mean rates of arrival at each detector of photons originating from each point within the body space. Two methods of constructing these weights are discussed, and reconstructions resulting from phantom and real data are presented.