933 resultados para estimation of parameters


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The project aimed to use results of contamination of city vegetation with heavy metals and sulphur compounds as the basis for analysing the integral response of trees and shrubs to contamination, through a complex method of phytoindication. The results were used to draw up recommendations on pollution reduction in the city and to develop the method of phytoindication as a means of monitoring environmental pollution in St. Petersburg and other large cities. Field investigations were carried out in August 1996, and 66 descriptions of green areas were made in order to estimate the functional state of plants in the Vasileostrovsky district. Investigations of the spectrum reflecting properties of plants showed considerable variation of albedo meanings of leaves under the influence of various internal and external factors. The results indicated that lime trees most closely reflect the condition of the environment. Practically all the green areas studied were in poor condition, the only exceptions being areas of ash trees, which are more resistant to environmental pollution, and one lime-tree alley in a comparatively unpolluted street. The study identified those types of trees which are more or less resistant to complex environmental pollution and Ms. Terekhina recommends that the species in the present green areas be changed to include a higher number of the more resistant species. The turbidimetric analysis of tree barks for sulphates gave an indication of the level and spatial distribution of each pollutant, and the results also confirmed other findings that electric conductivity is a significant feature in determining the extent of sulphate pollution. In testing for various metals, the lime tree showed the highest contents for all elements except magnesium, copper, zinc, cadmium and strontium, again confirming the species' vulnerability to pollution. Medium rates of concentration in the city and environs showed that city plants concentrate 3 times as many different elements and 10 times more chromium, copper and lead than do those in the suburbs. The second stage of the study was based on the concept of phytoindication, which presupposes that changes in the relation of chemical elements in regional biological circulation under the influence of technogenesis provide a criterion for predicting displacements in people's health. There are certain basic factors in this concept. The first is that all living beings are related ecologically as well as by their evolutionary origin, and that the lower an organism is on the evolutionary scale, the less adaptational reserve it has. The second is that smaller concentrations of chemical elements are needed for toxicological influence on plants than on people and so the former's reactions to geochemical factors are easier to characterise. Visual indicational features of urban plants are well defined and can form the basis of a complex "environment - public health" analysis. Specific plant reactions reflecting atmospheric pollution and other components of urbogeosystems make it possible to determine indication criteria for predicting possible disturbances in the general state of health of the population. Thirdly the results of phytoindication investigations must be taken together with information about public health in the area. It only proved possibly to analyse general indexes of public health based on statistical data from the late 1980s and early 1990s as the data of later years were greatly influenced by social factors. These data show that the rates of illness in St. Petersburg (especially for children) are higher than in Russia as a whole, for most classes of diseases, indicating that the population there is more sensitive to the ecological state of the urban environment. The Vasileostrovsky district had the second highest sick rate for adullts, while the rate of infant mortality in the first year of life was highest there. Ms. Terekhina recommends further studies to more precisely assess the effectiveness of the methods she tested, but has drawn up a proposed map of environmental hazard for the population, taking into account prevailing wind directions.

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OBJECTIVE: Computed tomography (CT) and magnetic resonance imaging (MRI) are introduced as an alternative to traditional autopsy. The purpose of this study was to investigate their accuracy in mass estimation of liver and spleen. METHODS: In 44 cases, the weights of spleen and liver were estimated based on MRI and CT data using a volume-analysis software and a postmortem tissue-specific density factor. In a blinded approach, the results were compared with the weights noted at autopsy. RESULTS: Excellent correlation between estimated and real weights (r = 0.997 for MRI, r = 0.997 for CT) was found. Putrefaction gas and venous air embolism led to an overestimation. Venous congestion and drowning caused higher estimated weights. CONCLUSION: Postmortem weights of liver and spleen can accurately be assessed by nondestructive imaging. Multislice CT overcomes the limitation of putrefaction and venous air embolism by the possibility to exclude gas. Congestion seems to be even better assessed.

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We investigate the interplay of smoothness and monotonicity assumptions when estimating a density from a sample of observations. The nonparametric maximum likelihood estimator of a decreasing density on the positive half line attains a rate of convergence at a fixed point if the density has a negative derivative. The same rate is obtained by a kernel estimator, but the limit distributions are different. If the density is both differentiable and known to be monotone, then a third estimator is obtained by isotonization of a kernel estimator. We show that this again attains the rate of convergence and compare the limit distributors of the three types of estimators. It is shown that both isotonization and smoothing lead to a more concentrated limit distribution and we study the dependence on the proportionality constant in the bandwidth. We also show that isotonization does not change the limit behavior of a kernel estimator with a larger bandwidth, in the case that the density is known to have more than one derivative.

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This paper discusses estimation of the tumor incidence rate, the death rate given tumor is present and the death rate given tumor is absent using a discrete multistage model. The model was originally proposed by Dewanji and Kalbfleisch (1986) and the maximum likelihood estimate of the tumor incidence rate was obtained using EM algorithm. In this paper, we use a reparametrization to simplify the estimation procedure. The resulting estimates are not always the same as the maximum likelihood estimates but are asymptotically equivalent. In addition, an explicit expression for asymptotic variance and bias of the proposed estimators is also derived. These results can be used to compare efficiency of different sacrifice schemes in carcinogenicity experiments.

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In recent years, researchers in the health and social sciences have become increasingly interested in mediation analysis. Specifically, upon establishing a non-null total effect of an exposure, investigators routinely wish to make inferences about the direct (indirect) pathway of the effect of the exposure not through (through) a mediator variable that occurs subsequently to the exposure and prior to the outcome. Natural direct and indirect effects are of particular interest as they generally combine to produce the total effect of the exposure and therefore provide insight on the mechanism by which it operates to produce the outcome. A semiparametric theory has recently been proposed to make inferences about marginal mean natural direct and indirect effects in observational studies (Tchetgen Tchetgen and Shpitser, 2011), which delivers multiply robust locally efficient estimators of the marginal direct and indirect effects, and thus generalizes previous results for total effects to the mediation setting. In this paper we extend the new theory to handle a setting in which a parametric model for the natural direct (indirect) effect within levels of pre-exposure variables is specified and the model for the observed data likelihood is otherwise unrestricted. We show that estimation is generally not feasible in this model because of the curse of dimensionality associated with the required estimation of auxiliary conditional densities or expectations, given high-dimensional covariates. We thus consider multiply robust estimation and propose a more general model which assumes a subset but not all of several working models holds.

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Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.