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Offers real-world applications to reliability and inference, data mining, bioinformatics, lifetime data, and neural networks
New results are emphasized with potential for solving real-world problems
Aimed at a broad audience of graduate students, researchers, and practitioners in statistics, mathematics, engineering, economics, social science, bioengineering, and bioscience
Accessible to graduate students yet also of interest to experts
An outgrowth of the 12th International Conference on Applied Stochastic Models and Data Analysis, this book is a collection of invited chapters presenting recent developments in the field of data analysis, with applications to reliability and inference, data mining, bioinformatics, lifetime data, and neural networks. Emphasized throughout the volume are new methods with the potential for solving real-world problems in various areas.
The book is divided into eight major sections:
* Data Mining and Text Mining
* Information Theory and Statistical Applications
* Asymptotic Behaviour of Stochastic Processes and Random Fields
* Bioinformatics and Markov Chains
* Life Table Data, Survival Analysis, and Risk in Household Insurance
* Neural Networks and Self-Organizing Maps
* Parametric and Nonparametric Statistics
* Statistical Theory and Methods
Advances in Data Analysis is a useful reference for graduate students, researchers, and practitioners in statistics, mathematics, engineering, economics, social science, bioengineering, and bioscience.
Content Level »Research
Keywords »Fitting - Generalized linear model - Markov chain - Measure - Non-parametric statistics - Parametric statistics - Resampling - STATISTICA - Sage - Survival analysis - best fit - bioinformatics - data analysis - databases - information theory
I Data Mining and Text Mining.- Assessing the Stability of Supplementary Elements on Principal Axes Maps Through Bootstrap Resampling. Contribution to Interpretation in Textual Analysis.- A Doubly Projected Analysis for Lexical Tables.- Analysis of a Mixture of Closed and Open-Ended Questions in the Case of a Multilingual Survey.- Number of Frequent Patterns in Random Databases.- II Information Theory and Statistical Applications.- Measures of Divergence in Model Selection.- High Leverage Points and Outliers in Generalized Linear Models for Ordinal Data.- On a Minimization Problem Involving Divergences and Its Applications.- III Asymptotic Behaviour of Stochastic Processes and Random Fields.- Remarks on Stochastic Models Under Consideration.- New Invariance Principles for Critical Branching Process in Random Environment.- Gaussian Approximation for Multichannel Queueing Systems.- Stochastic Insurance Models, Their Optimality and Stability.- Central Limit Theorem for Random Fields and Applications.- A Berry – Esseen Type Estimate for Dependent Systems on Transitive Graphs.- Critical and Subcritical Branching Symmetric Random Walks on -Dimensional Lattices.- IV Bioinformatics and Markov Chains.- Finite Markov Chain Embedding for the Exact Distribution of Patterns in a Set of Random Sequences.- On the Convergence of the Discrete-Time Homogeneous Markov Chain.- V Life Table Data, Survival Analysis and Risk in Household Insurance.- Comparing the Gompertz-Type Models with a First Passage Time Density Model.- A Comparison of Recent Procedures in Weibull Mixture Testing.- Hierarchical Bayesian Modelling of Geographic Dependence of Risk in Household Insurance.- VI Neural Networks and Self-Organizing Maps.- The FCN Framework: Development and Applications.- On the Use of Self-Organising Maps to Analyse Spectral Data.- Neuro-Fuzzy Versus Traditional Models for Forecasting Wind Energy Production.- VII Parametric and Non-parametric Statistics.- Nonparametric Comparison of Several Sequential -out-of- Systems.- Adjusting -Values when Is Large in the Presence of Nuisance Parameters.- VIII Statistical Theory and Methods.- Fitting Pareto II Distributions on Firm Size: Statistical Methodology and Economic Puzzles.- Application of Extreme Value Theory to Economic Capital Estimation.- Multiresponse Robust Engineering: Industrial Experiment Parameter Estimation.- Inference for Binomial Change Point Data.