Examples of the use of an equivalence theorem in constructing optimum experimental designs for random-effects nonlinear regression models

The problem considered is that of finding an optimum measurement schedule to estimate population parameters in a nonlinear model when the patient effects are random. The paper presents examples of the use of sensitivity functions, derived from the General Equivalence Theorem for D-optimality, in the construction of optimum population designs for such schedules. With independent observations, the theorem applies to the potential inclusion of a single observation. However, in population designs the observations are correlated and the theorem applies to the inclusion of an additional measurement schedule. In one example, three groups of patients of differing size are subject to distinct schedules. Numerical, as opposed to analytical, calculation of the sensitivity function is advocated. The required covariances of the observations are found by simulation.     

Form Perception of Partly Occluded Shapes in 4‐Month‐Old Infants

Two habituation experiments were conducted to investigate how 4‐month‐old infants perceive partly occluded shapes. In the first experiment, we presented a simple, partly occluded shape to the infants until habituation was reached. Then we showed either a probable completion (one that would be predicted on the basis of both local and global cues) or an improbable completion. Longer looking times were found for the improbably completed shape (compared to probable and control conditions), suggesting that the probable shape was perceived during partial occlusion. In the second experiment, infants were habituated to more ambiguous partly occluded shapes, where local and global cues would result in different completions. For adults, the percept of these shapes is usually dominated by global influences. However, after habituation the infants looked longer at the globally completed shapes. These results suggest that by the age of 4 months, infants are able to infer the perceptual completion of partly occluded shapes, but for more ambiguous shapes, this completion seems to be dominated by local influences.     

Framing Latina/o Immigration, Education, and Activism

Although Latinas/os have a long history in the USA and represent a growing percentage of the population, they remain largely invisible or stereotyped in dominant images and discourses. Such representations are often ahistorical, and they camouflage the effects of US power and inequality. However, the spring 2006 immigrant rights demonstrations disturbed dominant conceptions. The demonstrators called attention to the contradictory US practices that disrupt home countries, recruit labor migrants, and deny immigrants full participation. Likewise, the role of students in these demonstrations spurred reflections on why youth would walk out of their schools for immigrant rights. Inspired by these demonstrations, we combine materials from multiple disciplines to emphasize the significance of US imperialism, exploitation, and exclusion on Latina/o migration, education, and activism. Key to this article is a reframing of how the media, K-12 curriculum, and popular discourse often engage in a cultural cover-up that sustains inequality.     

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.     

Refined Inference on Long Memory in Realized Volatility

There is an emerging consensus in empirical finance that realized volatility series typically display long range dependence with a memory parameter (d) around 0.4 (Andersen et al., 2001; Martens et al., 2004). The present article provides some illustrative analysis of how long memory may arise from the accumulative process underlying realized volatility. The article also uses results in Lieberman and Phillips (2004, 2005) to refine statistical inference about d by higher order theory. Standard asymptotic theory has an O(n^-1/2) error rate for error rejection probabilities, and the theory used here refines the approximation to an error rate of o(n^-1/2). The new formula is independent of unknown parameters, is simple to calculate and user-friendly. The method is applied to test whether the reported long memory parameter estimates of Andersen et al. (2001) and Martens et al. (2004) differ significantly from the lower boundary (d = 0.5) of nonstationary long memory, and generally confirms earlier findings.     

Automobile prices and protection: The U.S.-Japan trade restraint

We examine the effects of the U.S.-Japan trade restraint on automobile prices and quality upgrading, for both Japanese imports and American small cars. From April 1981 to April 1984 the suggested retail price of all Japanese models increased by 15.8 percent, or 5.3 percent per year. We find that nearly the entire amount of this rise can be explained by the upgrading of individual models. This upgrading may benefit consumers who would purchase a luxury import in any case, but harms those who desire the basic imports. In addition to upgrading, we conclude that a second cost of the trade restraint has been to prevent the yen depreciation from being passed onto American consumers, in terms of lower imported auto prices. For U.S. small cars we find a 9.1 percent rise in the suggested retail price, or 3 percent per year, with a fraction of this amount due to model upgrading. If the yen depreciation had led to lower import prices without the trade restraint, then we expect that U.S. auto prices would have been lower, too.     

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.     

Empirical Non-Parametric Control Charts: Estimation Effects and Corrections

Owing to the extreme quantiles involved, standard control charts are very sensitive to the effects of parameter estimation and non-normality. More general parametric charts have been devised to deal with the latter complication and corrections have been derived to compensate for the estimation step, both under normal and parametric models. The resulting procedures offer a satisfactory solution over a broad range of underlying distributions. However, situations do occur where even such a large model is inadequate and nothing remains but to consider non- parametric charts. In principle, these form ideal solutions, but the problem is that huge sample sizes are required for the estimation step. Otherwise the resulting stochastic error is so large that the chart is very unstable, a disadvantage that seems to outweigh the advantage of avoiding the model error from the parametric case. Here we analyse under what conditions non-parametric charts actually become feasible alternatives for their parametric counterparts. In particular, corrected versions are suggested for which a possible change point is reached at sample sizes that are markedly less huge (but still larger than the customary range). These corrections serve to control the behaviour during in-control (markedly wrong outcomes of the estimates only occur sufficiently rarely). The price for this protection will clearly be some loss of detection power during out-of-control. A change point comes in view as soon as this loss can be made sufficiently small.     

Dementia Problem Behavior and the Production of Informal Caregiving Services

Informal caregivers of individuals with Alzheimer's disease spend a considerable amount of time providing care. In this paper, we use Grossman's health production and Becker's time allocation models to develop a model of informal care provision to elderly dementia patients. In our model, time inputs produce caregiving services, which provides utility to the caregiver, but reduces leisure. We assume that time is less productive of services on the margin as the disease progresses. In this framework, an increase in patients' disease severity does not necessarily increase informal caregiver time input. The cost of formal care establishes a reservation price for informal caregiving. When the costs of informal caregiving rise above this reservation level, the patient is institutionalized. We test empirically the effect of deterioration in the patients' condition, proxied by both disease severity and dementia problem behavior, on informal caregiving time. We find that dementia-related problem behaviors and functional limitations significantly increase inputs of informal caregiving time. Patients' problem behavior exerts a modifying effect on functional limitations, and patients' comorbidities have no effect on informal caregiving time.