Suchergebnisse

The dose to human and nonhuman individuals inflicted by anthropogenic radiation is an important issue in international and domestic policy. The current paradigm for nonhuman populations asserts that if the dose to the maximally exposed individuals in a population is below a certain criterion (e.g., <10 mGy d−1) then the population is adequately protected. Currently, there is no consensus in the regulatory community as to the best statistical approach. Statistics, currently considered, include the maximum likelihood estimator for the 95th percentile of the sample mean and the sample maximum. Recently, the investigators have proposed the use of the maximum likelihood estimate of a very high quantile as an estimate of dose to the maximally exposed individual. In this study, we compare all of the above‐mentioned statistics to an estimate based on extreme value theory. To determine and compare the bias and variance of these statistics, we use Monte Carlo simulation techniques, in a procedure similar to a parametric bootstrap. Our results show that a statistic based on extreme value theory has the least bias of those considered here, but requires reliable estimates of the population size. We recommend establishing the criterion based on what would be considered acceptable if only a small percentage of the population exceeded the limit, and hence recommend using the maximum likelihood estimator of a high quantile in the case that reliable estimates of the population size are not available.

The notion of cointegration has led to a renewed interest in the identification and estimation of structural relations among economic time series. This paper reviews the different approaches that have been put forward in the literature for identifying cointegrating relationships and imposing (possibly over‐identifying) restrictions on them. Next, various algorithms to obtain (approximate) maximum likelihood estimates and likelihood ratio statistics are reviewed, with an emphasis on so‐called switching algorithms. The implementation of these algorithms is discussed and illustrated using an empirical example.

AbstractChen and Bhattacharyya [Exact confidence bounds for an exponential parameter under hybrid censoring, Commun Statist Theory Methods 17 (1988), 1857–1870] considered a hybrid censoring scheme and obtained the exact distribution of the maximum likelihood estimator of the mean of an exponential distribution along with an exact lower confidence bound. Childs et al. [Exact likelihood inference based on Type‐I and Type‐II hybrid censored samples from the exponential distribution, Ann Inst Statist Math 55 (2003), 319–330] recently derived an alternative simpler expression for the distribution of the MLE. These authors also proposed a new hybrid censoring scheme and derived similar results for the exponential model. In this paper, we propose two generalized hybrid censoring schemes which have some advantages over the hybrid censoring schemes already discussed in the literature. We then derive the exact distribution of the maximum likelihood estimator as well as exact confidence intervals for the mean of the exponential distribution under these generalized hybrid censoring schemes. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004

AbstractDiffusion processes are commonly used to describe the dynamics of complex systems arising in a wide range of application fields. In this paper we propose, on the basis of diffusion processes, two models concerned with the stochastic behavior of fatigue cracks in a system. They are then used to get the distribution of the failure time, the first time the crack size of at least one of the cracks exceeds a given value. Several properties of our proposed models are presented, and the unknown parameters are estimated by the method of maximum likelihood. From these an estimate of failure time distribution is obtained. In this part, contrary to common practice, we do not assume availability of failure data. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2005

For a multilevel model with two levels and only a random intercept, the quality of different estimators of the random intercept is examined. Analytical results are given for the marginal model interpretation where negative estimates of the variance components are allowed for. Except for four or five level‐2 units, the Empirical Bayes Estimator (EBE) has a lower average Bayes risk than the Ordinary Least Squares Estimator (OLSE). The EBEs based on restricted maximum likelihood (REML) estimators of the variance components have a lower Bayes risk than the EBEs based on maximum likelihood (ML) estimators. For the hierarchical model interpretation, where estimates of the variance components are restricted being positive, Monte Carlo simulations were done. In this case the EBE has a lower average Bayes risk than the OLSE, also for four or five level‐2 units. For large numbers of level‐1 (30) or level‐2 units (100), the performances of REML‐based and ML‐based EBEs are comparable. For small numbers of level‐1 (10) and level‐2 units (25), the REML‐based EBEs have a lower Bayes risk than ML‐based EBEs only for high intraclass correlations (0.5).

This paper uses a panel probit model with simultaneous equations to explain the joint determination of de facto and de jure exchange rate regimes in developing countries since 1980. We also derive an ordered-choice panel probit model to explain the causes of discrepancies between the two regime choices. Both models are estimated using simulation-based maximum likelihood methodsl. The results of the simultaneous equations model suggest that the two regime choices are dependent of each other and exhibit considerable state dependence. The ordered probit model provides evidence that regime discrepancies reflect an error-correction mechanism, and the discrepancies are persistent over time.

Log-linear modeling is a popular statistical tool for analysing a contingency
table. This presentation focuses on an alternative approach to modeling ordinal categorical
data. The technique, based on orthogonal polynomials, provides a much simpler
method of model fitting than the conventional approach of maximum likelihood estimation,
as it does not require iterative calculations nor the fitting and re-fitting to search
for the best model. Another advantage is that quadratic and higher order effects can
readily be included, in contrast to conventional log-linear models which incorporate linear
terms only.The focus of the discussion is the application of the new parameter estimation technique
to multi-way contingency tables with at least one ordered variable. This will also
be done by considering singly and doubly ordered two-way contingency tables. It will
be shown by example that the resulting parameter estimates are numerically similar to
corresponding maximum likelihood estimates for ordinal log-linear models.

In this paper we apply a statistical model combining a random coefficient regression model and a latent class regression model. The EM-algorithm is used for maximum likelihood estimation of the unknown parameters in the model and it is pointed out how this leads to a straightforward handling of a number of different variance/covariance restrictions. Finally, the model is used to analyze how consumers' preferences for eight coffee samples relate to sensory characteristics of the coffees. Within this application the analysis corresponds to a model-based version of the so-called external preference mapping.

Using data from the evaluation of the Fast Track intervention, this article illustrates three methods for handling attrition. Multiple imputation and ignorable maximum likelihood estimation produce estimates that are similar to those based on listwise-deleted data. A panel selection model that allows for selective dropout reveals that highly aggressive boys accumulate in the treatment group over time and produces a larger estimate of treatment effect. In contrast, this model produces a smaller treatment effect for girls. The article's conclusion discusses the strengths and weaknesses of the alternative approaches and outlines ways in which researchers might improve their handling of attrition.

Abstract
This paper uses a maximum likelihood procedure that accounts for unobserved heterogeneity in the sample to implement a preference-based model to assess factors that influence parents' likelihood of losing their composure and physically abusing their children. A basic supposition of the model is that parents prefer to deal with parent-child conflict by choosing tactics and behaviors that do not exceed a specified level of violence; however, endogenous parent and child behaviors and exogenous circumstances may arouse parents' emotions that cause this level to be exceeded. Our results suggest policy interventions that influence such circumstances and associated behaviors may strongly influence the incidence of physical child abuse. We estimate the ex ante annual value parents attach to risk reductions of self-composure losses associated with excessive parent-child violence. This value is shown to be greater than currently estimated annual savings in ex post costs associated with a comparable decrease in U.S. physical child abuse incidence.

The paper develops an empirical no-arbitrage Gaussian affine term structure model to explain the dynamics of the German term structure of interest rates from 1979 through 1998. In contrast to most affine term structure models two risk factors that drive the dynamics are linked to observable macroeconomics factors: output and inflation. The results obtained by a Kalman-filter-based maximum likelihood procedure indicate that the dynamics of the German term structure of interest rates can be sufficiently explained by expected variations in those macroeconomic factors plus an additional unobservable factor. Furthermore, we are able to extract a monetary policy reaction function within this no-arbitrage model of the term structure that closely resembles the empirical reaction functions that are based on the dynamics of the short rate only.

Why do women have more difficulty pursuing research careers than men? Although this topic has been extensively investigated in industrialized countries, prior studies provide little comparative evidence from less-developed areas. Based on a survey of 293 scientists in Ghana, Kenya, and the Indian state of Kerala, this article examines gender differences on a variety of individual, social, and organizational dimensions. The results show small or nonexistent differences between women and men in individual characteristics, professional resources, and the organizational conditions under which research is conducted. The article argues that a combination of educational and research localism increases the likelihood of restricted professional networks for women. Gender inequality in the research systems of the developing world may be based on systemic deficits in the acquisition of social rather than material resources. The most important implication is that educational policy for development should focus on international opportunities for women in the near future.

Spanish regulation on dam safety recommends the estimation of a return period of 1000 years for the Design Flood and of 10,000 for the Safety Check Flood. The criteria used for the calculations are based on Flood frequency analysis (FFA) from gauge record data or on the Probable Maximum Flood (PMF) which is the hypothetical maximums of rainfall turned into runoff. The short gauge records affords little support for the FFA and for the hypothetical PMF, which in addition the absence of a probability associated, limits the utility of these indices for risk-based dam safety decisions. Palaeoflood techniques are a means to directly assess the probability of extreme floods and test the validity of the PMF-based models. In this paper, palaeoflood record was applied to a case study in the Guadalentín river, upstream of the Valdeinfierno reservoir (372 km2). The Valdeinfierno dam spillway has a capacity of 550 m3s-1. (design flood). In addition, available historical flood data was collected. Palaeoflood data, together with the gauge record, was combined for the FFA, using existing methods for the fitting of distribution functions, such as the maximum likelihood. These return periods are compared with the PMF results. Palaeoflood analysis provides a discharge of 2350 m3s-1 for the design flood (1000-yr return period), and 3450 m3s-1 for the safety check flood (10,000 yr-return period). Some recommendations for the calculation of the safety check flood and design flood are provided which can improve existing legislation on dam safety. ; Este trabajo se ha realizado con la financiación del Proyecto CICYT REN2001-1633/RIES "Incorporación de datos de paleocrecidas e inundaciones históricas al cálculo de la avenida de diseño de presas" (PALEOCAP). ; Peer Reviewed

This volume focuses on econometric models that confront estimation and inference issues occurring when sample data exhibit spatial or spatiotemporal dependence. This can arise when decisions or transactions of economic agents are related to the behaviour of nearby agents. Dependence of one observation on neighbouring observations violates the typical assumption of independence made in regression analysis. Contributions to this volume by leading experts in the field of spatial econometrics provide details regarding estimation and inference based on a variety of econometric methods including, maximum likelihood, Bayesian and hierarchical Bayes, instrumental variables, generalized method of moments, maximum entropy, non-parametric and spatiotemporal. An overview of spatial econometric models and methods is provided that places contributions to this volume in the context of existing literature. New methods for estimation and inference are introduced in this volume and Monte Carlo comparisons of existing methods are described. In addition to topics involving estimation and inference, approaches to model comparison and selection are set forth along with new tests for spatial dependence and functional form. These methods are applied to a variety of economic problems including: hedonic real estate pricing, agricultural harvests and disaster payments, voting behaviour, identification of edge cities, and regional labour markets. The volume is supported by a web site containing data sets and software to implement many of the methods described by contributors to this volume

Previous research suggests that American adolescents usually have ready access to guns, and that the extent of misuse of guns by adolescents is not much affected by local gun prevalence or regulation. This "futility" claim is based on one interpretation of survey data from several cities, but has not been tested directly. Here we do so using microdata from a nationally representative survey, the 1995 National Survey of Adolescent Males. Using the restricted geo‐coded version of these data, and conditioning on an extensive set of covariates, we find (among other results) that the likelihood of gun carrying increases markedly with the prevalence of gun ownership in the given community. We also analyze the propensity to carry other types of weapons, finding that it is unrelated to the local prevalence of gun ownership. The prevalence of youths carrying both guns and other weapons is positively related to the local rate of youth violence (as measured by the robbery rate), confirmatory evidence that weapons carrying by youths is motivated in part by self‐protection.