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Traditional Statistical Approaches (traditional + statistical_approach)
Selected AbstractsDesign of the pH Profile for Asymmetric Bioreduction of Ethyl 4-Chloro-3-oxobutyrate on the Basis of a Data-Driven MethodBIOTECHNOLOGY PROGRESS, Issue 6 2002Junghui Chen The goal of this paper was to design the optimal time-varying operating pH profile in the asymmetric reduction of ethyl 4-chloro-3-oxobutyrate by baker's yeast. Ethyl ( S)-4-chloro-3-hydroxybutyrate was produced to reach two important quality indices: reaction yield and product optical purity. The method integrated an orthogonal function approximation and an orthogonal array. The technique used a set of orthonormal functions as the basis for representing the possible profile. The optimal profile could be obtained if the orthogonal coefficients were properly adjusted. The orthogonal array was used to design and analyze the effect of each orthogonal coefficient in order to reach the optimal objective (quality) function. The performance based on the proposed strategy was significantly improved by over 10% compared with the traditional fixed pH or uncontrolled pH values during the reaction. The proposed method can be applied to the required dynamic profile in the bioreactor process to effectively improve the product quality, given good design directions and the advantage of the traditional statistical approach. [source] A simulation approach to determine statistical significance of species turnover peaks in a species-rich tropical cloud forestDIVERSITY AND DISTRIBUTIONS, Issue 6 2007K. Bach ABSTRACT Use of ,-diversity indices in the study of spatial distribution of species diversity is hampered by the difficulty of applying significance tests. To overcome this problem we used a simulation approach in a study of species turnover of ferns, aroids, bromeliads, and melastomes along an elevational gradient from 1700 m to 3400 m in a species-rich tropical cloud forest of Bolivia. Three parameters of species turnover (number of upper/lower elevational species limits per elevational step, Wilson,Shmida similarity index between adjacent steps) were analysed. Significant species turnover limits were detected at 2000 (± 50) m and 3050 m, which roughly coincided with the elevational limits of the main vegetation types recognized in the study area. The taxon specificity of elevational distributions implies that no single plant group can be used as a reliable surrogate for overall plant diversity and that the response to future climate change will be taxon-specific, potentially leading to the formation of plant communities lacking modern analogues. Mean elevational range size of plant species was 490 m (± 369). Elevational range sizes of terrestrial species were shorter than those of epiphytes. We conclude that our simulation approach provides an alternative approach for assessing the statistical significance of levels of species turnover along ecological gradient without the limitations imposed by traditional statistical approaches. [source] On the Application of Inductive Machine Learning Tools to Geographical AnalysisGEOGRAPHICAL ANALYSIS, Issue 2 2000Mark Gahegan Inductive machine learning tools, such as neural networks and decision trees, offer alternative methods for classification, clustering, and pattern recognition that can, in theory, extend to the complex or "deep" data sets that pervade geography. By contrast, traditional statistical approaches may fail, due to issues of scalability and flexibility. This paper discusses the role of inductive machine learning as it relates to geographical analysis. The discussion presented is not based on comparative results or on mathematical description, but instead focuses on the often subtle ways in which the various inductive learning approaches differ operationally, describing (1) the manner in which the feature space is partitioned or clustered, (2) the search mechanisms employed to identify good solutions, and (3) the different biases that each technique imposes. The consequences arising from these issues, when considering complex geographic feature spaces, are then described in detail. The overall aim is to provide a foundation upon which reliable inductive analysis methods can be constructed, instead of depending on piecemeal or haphazard experimentation with the various operational criteria that inductive learning tools call for. Often, it would appear that these criteria are not well understood by practitioners in the geographic sphere, which can lead to difficulties in configuration and operation, and ultimately to poor performance. [source] A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress PredictionJOURNAL OF RISK AND INSURANCE, Issue 3 2006Patrick L. Brockett This study examines the effect of the statistical/mathematical model selected and the variable set considered on the ability to identify financially troubled life insurers. Models considered are two artificial neural network methods (back-propagation and learning vector quantization (LVQ)) and two more standard statistical methods (multiple discriminant analysis and logistic regression analysis). The variable sets considered are the insurance regulatory information system (IRIS) variables, the financial analysis solvency tracking (FAST) variables, and Texas early warning information system (EWIS) variables, and a data set consisting of twenty-two variables selected by us in conjunction with the research staff at TDI and a review of the insolvency prediction literature. The results show that the back-propagation (BP) and LVQ outperform the traditional statistical approaches for all four variable sets with a consistent superiority across the two different evaluation criteria (total misclassification cost and resubstitution risk criteria), and that the twenty-two variables and the Texas EWIS variable sets are more efficient than the IRIS and the FAST variable sets for identification of financially troubled life insurers in most comparisons. [source] | ||||||||||