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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Environmental and ecological statistics 1 (1994), S. 84-85 
    ISSN: 1573-3009
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Environmental monitoring and assessment 5 (1985), S. 137-154 
    ISSN: 1573-2959
    Source: Springer Online Journal Archives 1860-2000
    Topics: Energy, Environment Protection, Nuclear Power Engineering
    Notes: Abstract During the summer of 1982, the U.S. Environmental Protection Agency conducted a comprehensive multimedia environmental monitoring program in the vicinity of two secondary lead smelters and at one reference site in Dallas, Texas. This monitoring program included a major soils investigation with the collection and analysis of over 2700 soil samples. In addition, approximately 1000 blood and 840 house dust samples were collected and analyzed for their lead content. Utilizing geostatistics and the data obtained from a portion of the soil samples, isopleths of constant soil lead concentrations within each of the three monitoring sites were identified. Presented are the soil monitoring strategy and the geostatistical techniques selected for identifying the distributions of soil lead patterns at each of the three monitoring sites. In addition, the soil sampling and analytical methods used, the sample handling and preparation procedures, and the geostatistical results obtained are identified.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 5 (1991), S. 309-319 
    ISSN: 0886-9383
    Keywords: Confidence intervals ; Products of normal random variables ; Risk/exposure modeling ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: In many environmental applications, such as exposure assessment and risk modelling, the desired estimate is a random variable computed as the product of three independently distributed random variables. These variables may not necessarily have the same mean and variance. The method for finding the 100(1 - α)% confidence interval for the mean of the product random variable has been proposed by some practitioners as the product of the 100(1 - α)% confidence interval of the three means. In this paper we show that the distribution of the product of three independent normal variables is not normal. We find the mean and variance of the product distribution. Further, we show that although the mean of the product is equal to the product of the means, the product of the three confidence intervals is not a good approximation of the confidence intervals for the mean of the product variable. The confidence interval of the mean of the product variable may be estimated by computer simulation. An algorithm for estimating the confidence interval for the mean of the product random variable is given. The program implementing this algorithm is given as an appendix.
    Type of Medium: Electronic Resource
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  • 4
    Electronic Resource
    Electronic Resource
    Springer
    Mathematical geology 20 (1988), S. 667-672 
    ISSN: 1573-8868
    Keywords: Autocorrelation ; fractals ; random functions ; semivariogram
    Source: Springer Online Journal Archives 1860-2000
    Topics: Geosciences , Mathematics
    Notes: Abstract Methods suggested in the past for simulated ore concentration or pollution concentration over an area of interest, subject to the condition that the simulated surface is passing through specifying points, are based on the assumption of normality. A new method is introduced here which is a generalization of the subdivision method used in fractals. This method is based on the construction of a fractal plane-to-line functionf(x, y, R, e, u), where(x, y) is in[a, b]×[c, d], R is the autocorrelation function,e is the resolution limit, andu is a random real function on [−1, 1]. The simulation using fractals escapes from any distribution assumptions of the data. The given network of points is connected to form quadrilaterals; each one of the quadrilaterals is split based on ways which are extensions of the well-known subdivision method. The quadrilaterals continue to split and grow until resolution obtained in bothx andy directions is smaller than a prespecified resolution. If thex coordinate of theith quadrilateral is in[a i ,b i ] and they coordinate is in[c i ,d i ], the growth of this quadrilateral is a function of(b i −a i ) and(d i −c i ); the quadrilateral could grow toward the positive or negativez axis with equal probability forming four new quadrilaterals having a common vertex.
    Type of Medium: Electronic Resource
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