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Multinomial Model (multinomial + model)

Distribution by Scientific Domains

Mathematics and Statistics	60%
Business, Economics, Finance and Accounting	40%

Selected Abstracts

The multi-clump finite mixture distribution and model selection

ENVIRONMETRICS, Issue 2 2010
Sudhir R. Paul
Abstract In practical data analysis, often an important problem is to determine the number of clumps in discrete data in the form of proportions. This can be done through model selection in a multi-clump finite mixture model. In this paper, we propose bootstrap likelihood ratio tests to test the fit of a multinomial model against the single clump finite mixture distribution and to determine the number of clumps in the data, that is, to select a model with appropriate number of clumps. Shortcomings of some traditional large sample procedures are also shown. Three datasets are analyzed. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Determinants of participation and nonparticipation in job-related education and training in Shenzhen, China

HUMAN RESOURCE DEVELOPMENT QUARTERLY, Issue 4 2004
Jin Xiao
In the fast-growing market-oriented economy of Shenzhen, China, most employees have continued to participate in job-related education and training. We argue that as firms have acquired autonomy in their operations and individuals have gained the right to pursue their personal occupational aspirations, non,state-sponsored education and training systems for the working population have developed to respond to the demands from firms, as well as individuals. With survey data from 3,475 employees in seventy-six firms from Shenzhen, this study uses a multinomial model to examine patterns in employee participation in job-related education and training. There are basically four options open to employees: taking part in education and training provided by a firm to its own employees, enrolling in education and training offered by institutions outside the firm, availing themselves of both options simultaneously, or not participating. Our findings suggest that these four groups of employees vary in terms of their cultural and symbolic attributes, their individual socioeconomic attributes in relation to their workplace, and the economic attributes of their firm. [source]

A MULTINOMIAL APPROXIMATION FOR AMERICAN OPTION PRICES IN LÉVY PROCESS MODELS

MATHEMATICAL FINANCE, Issue 4 2006
Ross A. Maller
This paper gives a tree-based method for pricing American options in models where the stock price follows a general exponential Lévy process. A multinomial model for approximating the stock price process, which can be viewed as generalizing the binomial model of Cox, Ross, and Rubinstein (1979) for geometric Brownian motion, is developed. Under mild conditions, it is proved that the stock price process and the prices of American-type options on the stock, calculated from the multinomial model, converge to the corresponding prices under the continuous time Lévy process model. Explicit illustrations are given for the variance gamma model and the normal inverse Gaussian process when the option is an American put, but the procedure is applicable to a much wider class of derivatives including some path-dependent options. Our approach overcomes some practical difficulties that have previously been encountered when the Lévy process has infinite activity. [source]

Meta-Analysis of Diagnostic Test Accuracy Studies with Multiple Thresholds using Survival Methods

BIOMETRICAL JOURNAL, Issue 1 2010
H. Putter
Abstract Diagnostic tests play an important role in clinical practice. The objective of a diagnostic test accuracy study is to compare an experimental diagnostic test with a reference standard. The majority of these studies dichotomize test results into two categories: negative and positive. But often the underlying test results may be categorized into more than two, ordered, categories. This article concerns the situation where multiple studies have evaluated the same diagnostic test with the same multiple thresholds in a population of non-diseased and diseased individuals. Recently, bivariate meta-analysis has been proposed for the pooling of sensitivity and specificity, which are likely to be negatively correlated within studies. These ideas have been extended to the situation of diagnostic tests with multiple thresholds, leading to a multinomial model with multivariate normal between-study variation. This approach is efficient, but computer-intensive and its convergence is highly dependent on starting values. Moreover, monotonicity of the sensitivities/specificities for increasing thresholds is not guaranteed. Here, we propose a Poisson-correlated gamma frailty model, previously applied to a seemingly quite different situation, meta-analysis of paired survival curves. Since the approach is based on hazards, it guarantees monotonicity of the sensitivities/specificities for increasing thresholds. The approach is less efficient than the multinomial/normal approach. On the other hand, the Poisson-correlated gamma frailty model makes no assumptions on the relationship between sensitivity and specificity, gives consistent results, appears to be quite robust against different between-study variation models, and is computationally very fast and reliable with regard to the overall sensitivities/specificities. [source]

The Log Multinomial Regression Model for Nominal Outcomes with More than Two Attributes

BIOMETRICAL JOURNAL, Issue 6 2007
L. Blizzard
Abstract An estimate of the risk or prevalence ratio, adjusted for confounders, can be obtained from a log binomial model (binomial errors, log link) fitted to binary outcome data. We propose a modification of the log binomial model to obtain relative risk estimates for nominal outcomes with more than two attributes (the "log multinomial model"). Extensive data simulations were undertaken to compare the performance of the log multinomial model with that of an expanded data multinomial logistic regression method based on the approach proposed by Schouten et al. (1993) for binary data, and with that of separate fits of a Poisson regression model based on the approach proposed by Zou (2004) and Carter, Lipsitz and Tilley (2005) for binary data. Log multinomial regression resulted in "inadmissable" solutions (out-of-bounds probabilities) exceeding 50% in some data settings. Coefficient estimates by the alternative methods produced out-of-bounds probabilities for the log multinomial model in up to 27% of samples to which a log multinomial model had been successfully fitted. The log multinomial coefficient estimates generally had lesser relative bias and mean squared error than the alternative methods. The practical utility of the log multinomial regression model was demonstrated with a real data example. The log multinomial model offers a practical solution to the problem of obtaining adjusted estimates of the risk ratio in the multinomial setting, but must be used with some care and attention to detail. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]