Book Reviews

Books reviewed in this article:
LELE, S. R. and RICHTSMEIER, J. T. An Invariant Approach to the Statistical Analysis of Shapes.
HEYDE, C. C. and SENETA, E. (editors). Statisticians of the Centuries.
LAWSON, A. B. Statistical Methods in Spatial Epidemiology.
EVERITT, B. S. and PICKLES, A. Statistical Aspects of the Design and Analysis of Clinical Trials.
KERR, S. R. and DICKIE, L. M. The Biomass Spectrum: A Predator-Prey Theory of Aquatic Production.
DER, G. and EVERITT, B. S. A Handbook of Statistical Analyses Using SAS, 2nd Edition.
HAND, D., MANNILA, H. and SMYTH, P. Principles of Data Mining.
INDRAYAN, A. and SARMUKADDAM, S. B. Medical Biostatistics.
EWENS, W. J. GRANT, G. R. Statistical Methods in Bioinformatics: An Introduction.
HARRELL, F. E. Regression Modeling Strategies with Applications to Linear Models, Logistic Regression and Survival Analysis.
CONGDON, P. Bayesian Statistical Modelling.
CAMAZINE, S., DENEUBOURG, J.-L., FRANKS, N. R., SNEYD, J., THERAULAZ, G. and BONABEAU, E. Self-Organization in Biological Systems.     

Multivariable Model-building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables by ROYSTON, P. and SAUERBREI, W.

Multivariable Model‐building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables (P. Royston and W. Sauerbrei) David W. Hosmer Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (A. J. Izenman) Debashis Ghosh A Primer on Linear Models (J. F. Monahan) Muni S. Srivastava Mixed Effects Models and Extensions in Ecology with R (A. F. Zuur, E. N. Ieno, N. J. Walker, A. A. Saveliev, and G. M. Smith) Carl James Schwarz Random Effect and Latent Variable Model Selection (D. B. Dunson, Editor) Kenneth Rice Missing Data in Clinical Studies (G. Molenberghs and M. G. Kenward) Suzanne R. Dubnicka Functional and Operatorial Statistics (S. Dabo‐Niang and F. Ferraty, Editors) Jason D. Nielsen Statistical Analysis and Modelling of Spatial Point Patterns (J. Illian, A. Penttinen, H. Stoyan, and D. Stoyan) Jesper Møller Solved Problem in Geostatistics (O. Leuangthong, K. D. Khan, and C. V. Deutsch) Ole F. Christensen Statistical Methods for Environmental Epidemiology with R : A Case Study in Air Pollution and Health (R. D. Peng and F. Dominici) David Buckeridge Correspondence Analysis in Practice, Second Edition (M. Greenacre) Willem Heiser Introduction to Statistical Mediation Analysis (D. P. MacKinnon) Tyler J. VanderWeele Brief Reports by the Editor The EM Algorithm and Extensions, 2nd edition (G. J. McLachlan and T. Krishnan) Probability Models for DNA Sequence Evolution, 2nd edition (R. Durrett) Bayesian Methods for Data Analysis, 3rd edition (B. P. Carlin and T. A. Louis) Nonlinear Regression with R (C. Ritz and J. C. Streibig) Cancer Mortality and Morbidity Patterns in the U.S. Population: An Interdisciplinary Approach (K. G. Manton, I. Akushevich, and J. Kravchenko)     

Applied Spatial Data Analysis with R. by BIVAND, R. S., PEBESMA, E. J., and GOMEZ-RUBIO, V.

Applied Spatial Data Analysis with R (R. S. Bivand, E. J. Pebesma, and V. Gomez‐Rubio) Jay M. Ver Hoef Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (A. B. Lawson) J. Law Disease Surveillance: A Public Health Informatics Approach (J. S. Lombardo and D. L. Buckeridge, editors) Iris Pigeot Survival Analysis for Epidemiologic and Medical Research (S. Selvin) M. G. Valsecchi Survival and Event History Analysis: A Process Point of View (O. O. Aalen, O. Borgan, and H. K. Gjessing) Patricia Grambsch Nonlinear Dimensionality Reduction (J. A. Lee and M. Verleysen) Haonan Wang Model Selection and Model Averaging (G. Claeskens and N. L. Hjort) Thomas M. Loughin Meta‐Analysis of Binary Data Using Profile Likelihood (D. Böhning, R. Kuhnert, and S. Rattanasiri) Eloise Kaizar Wavelet Methods in Statistics with R (G. P. Nason) Jeffrey S. Morris Adaptive Design Theory and Implementation Using SAS and R (M. Chang) Feifang Hu Ecological Models and Data in R (B. M. Bolker) Rachel M. Fewster Applied Multiway Data Analysis (P. M. Kroonenberg) Renato Coppi Brief Reports by the Editor Sampling of Populations: Methods and Applications, 4th edition (P. S. Levy and S. Lemeshow) Applied Survival Analysis: Regression Modeling of Time‐to‐Event Data, 2nd edition (D. W. Hosmer, S. Lemeshow, and S. May) SAS for Data Analysis: Intermediate Statistical Methods (M. G. Marasinghe and W. J. Kennedy) Advances in Mathematical and Statistical Modeling (B. C. Arnold, N. Balakrishnan, J. M. Sarabia, and R. Mínguez, editors) An Introduction to Generalized Linear Models, 3rd edition (A. J. Dobson and A. G. Barnett) Design and Analysis of Bioavailability and Bioequivalence Studies, 3rd edition (S.‐C. Chow and J.‐P. Liu)     

Design and Analysis of Vaccine Studies by HALLORAN,M. E., LONGINI JR., I. M., and STRUCHINER,C. J.

Design and Analysis of Vaccine Studies (M. E. Halloran, I. M. Longini Jr. and C. J. Struchiner) Neal Alexander A SAS/IML Companion for Linear Models (J. J. Perrett) James E. Gentle Introducing Monte Carlo Methods with R (C. P. Robert and G. Casella) Brian D. Ripley Introductory Time Series with R (P. S. P. Cowpertwait and A. V. Metcalfe)
W. K. Li Linear Model Methodology (A. I. Khuri) Ronald R. Hocking     

Design of Comparative Experiments (Cambridge Series in Statistical and Probabilistic Mathematics) by BAILEY,R. A.

Design of Comparative Experiments (Cambridge Series in Statistical and Probabilistic Mathematics) (R. A. Bailey) Carla L. Goad Cluster Randomised Trials (R. J. Hayes and L. H. Moulton) Michael J. Campbell Bayesian Evaluation of Informative Hypotheses (H. Hoijtink, I. Klugkist, and P. A. Boelen, Editors) Bo Cai Bayesian Methods for Measures of Agreement (L. Broemeling) Cody Hamilton Statistical Learning from a Regression Perspective (R. A. Berk) Yutaka Yasui and Xiaoming Wang Statistical Detection and Surveillance of Geographic Clusters (P. Rogerson and I. Yamada) Maria Dolores Ugarte Global Sensitivity Analysis. The Primer (A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola) Bryan E. Shepherd Semi‐Markov Chains and Hidden Semi‐Markov Models toward Applications: Their Use in Reliability and DNA Analysis (V. S. Barbu and N. Limnios) Yann Guédon Brief Reports by the Editor Handbook of Statistical Analyses Using SAS , 3rd edition (G. Der and B. S. Everitt) Software for Data Analysis: Programming with R (J. M. Chambers) R for SAS and SPSS Users (R. A. Muenchen) Recent Advances in Linear Models and Related Areas: Essays in Honour of Helge Toutenburg (Shalabh and C. Heumann) Medical Statistics from A to Z, 2nd edition (B. Everitt)     

Frailty Models in Survival Analysis by WIENKE,A.

Frailty Models in Survival Analysis (A. Wienke) David Oakes Logistic Regression Models (J. M. Hilbe) Annette J. Dobson Design of Experiments: An Introduction Based on Linear Models (M. D. Morris) Gary W. Oehlert Models and Judgment for Valid Comparisons (H. I. Weisberg, Bias and Causation,) Judea Pearl     

Book Reviews

McCONWAY, K. J., JONES, M. C., and TAYLOR, P. C. Statistical Modelling using Genstat. SCHINAZI, R. B. Classical and Spatial Point Processes. PERRY, J. E., SMITH, R. H., WOIWOD, I. P., and MORSE, D. R. (editors). Chaos in Real Data: The Analysis of Non‐linear Dynamics from Short Ecological Time Series. BURTON, R. F. Physiology by Numbers, 2nd edition. BERLINER, L. M., NYCHKA, D., and HOAR, T. (editors). Studies in the Atmospheric Sciences. PRAKASA RAO, B. L. S. Statistical Inference for Diffusion Type Processes. DONNER, A. and KLAR, N. Design and Analysis of Cluster Randomization Trials in Health Research. MATIS, J. H. and KIFFE, T. R. Stochastic Population Models: A Compartmental Perspective. LINDSEY, J. K. Models for Repeated Measurements, 2nd edition. FORD, E. D. Scientific Method for Ecological Research. BURNHAM, K. P. and ANDERSON, D. R. Model Selection and Inference: A Practical Information‐Theoretic Approach. COX, D. R. and REID, N. The Theory of the Design of Experiments. PINHEIRO, J. C. and BATES, D. M. Mixed‐effects models in S and S‐PLUS. VENABLES, W. N. and RIPLEY, B. D. S Programming. KRAUSE, A. and OLSON, M. The Basics of S and S‐PLUS. CHEN, M.‐H., SHAO, Q.‐M., and IBRAHIM, J. G. Monte Carlo Methods in Bayesian Computation. SAHAI, H. and AGEEL, M. L. The Analysis of Variance: Fixed, Random, and Mixed Models. EVERITT, B. S. and DUNN, G. Statistical Analysis of Medical Data: New Developments. MUKHOPADHYAY, N. Probability and Statistical Inference. KNIGHT, K. Mathematical Statistics. Brief reports by the editor MILLER, R. E. Optimization: Foundations and Applications. BLAND, M. An Introduction to Medical Statistics, 3rd edition. RABE‐HESKETH, S. and EVERITT, B. A Handbook of Statistical Analyses using Stata, 2nd edition. KOO, J. O. (editor). American Series in Mathematical and Management Sciences Volume 42. Modern Mathematical, Management, and Statistical Sciences, The Index to the 20th Century, Prologue to the 21st Century. MISHRA, S. N. and SHARMA, B. D. (editors). American Series in Mathematical and Management Sciences Volume 43. FIM‐I, Forum for Interdisciplinary Mathematics Proceedings on Statistical Inference, Combinatorics and Related Areas, Volume I of Proceedings of Banaras Hindu University (BHU) Conference (Varanasi, India, December 1997). VENABLES, W. N. and RIPLEY, B. D. Modern Applied Statistics with S‐PLUS, 3rd edition.     

An Introduction to Statistical Inference and Its Applications with R by TROSSET,M. W.

An Introduction to Statistical Inference and Its Applications with R (M. W. Trosset) W. N. Venables ROC Curves for Continuous Data (W. J. Krzanowski and D. J. Hand) Lori E. Dodd A Guide to QTL Mapping with R/qtl (K. W. Broman and S. Sen) R. W. Doerge Design and Analysis of Clinical Trials with Time‐to‐Event Endpoint (K. E. Peace, ed.) Yu Shyr Translational Medicine: Strategies and Statistical Methods (D. Cosmatos and S.‐C. Chow) Yongzhao Shao Clinical Prediction Models: A Practical Approach to Development, Validation and Updating (E. W. Steyerberg) Rumana Omar Maximized Penalized Likelihood Estimation: Volume II: Regression (P. P. Eggermont and V. N. LaRicca) Hao Zhang Morphometrics with R (J. Claude) Alfred Kume Statistical Analysis of Network Data: Methods and Models (E. D. Kolaczyk) Crystal Linkletter     

Semiparametric Theory and Missing Data by TSIATIS, A. A.

Semiparametric Theory and Missing Data (A. A. Tsiatis) Andrea Rotnitzky Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis (M. J. Daniels and J. W. Hogan) Daniel F. Heitjan Bayesian Biostatistics and Diagnostic Medicine (L. D. Broemeling) Paul Gustafson Statistics in the Pharmaceutical Industry, 3rd edition (C. R. Buncher and J.‐Y. Tsay, Editors) Ralph B. D'Agostino Jr. Introduction to Machine Learning and Bioinformatics (S. Mitra, S. Datta, T. Perkins, and G. Michailidis) Yulan Liang The Statistics of Gene Mapping (D. Siegmund and B. Yakir) Hongyu Zhao DNA Methylation Microarrays: Experimental Design and Statistical Analysis (S.‐C. Wang and A. Petronis) Kimberly D. Siegmund Multiple Testing Procedures with Applications to Genomics (S. Dudoit and M. J. van der Laan) Ruth Heller The Statistical Analysis of Functional MRI Data (N. A. Lazar) Wesley K. Thompson Simulation and Inference for Stochastic Differential Equations with R Examples (S. M. Iacus) Dave Campbell Nonparametric Analysis of Univariate Heavy‐Tailed Data: Research and Practice (N. Markovich) M. Ivette Gomes Time Series Analysis with Applications in R, 2nd edition (J. D. Cryer and K.‐S. Chan) Timothy D. Johnson Brief Reports by the Editor Analysis of Variance and Covariance: How to Choose and Construct Models for the Life Sciences (C. P. Doncaster and A. J. H. Davey) Computational Statistics Handbook with MATLAB ^®, 2nd edition (W. L. Martinez and A. R. Martinez) Models for Probability and Statistical Inference: Theory and Applications (J. H. Stapleton) Medical Biostatistics, 2nd edition (A. Indrayan) Computational Methods in Biomedical Research (R. Khattree and D. N. Naik, Editors)     

Book Reviews

OWEN, A. B. Empirical Likelihood. GORE, A. and PARANJPE, S. A Course in Mathematical and Statistical Ecology. PAWITAN, Y. In All Likelihood: Statistical Modelling and Inference Using Likelihood. GASTWIRTH, J. L. (editor). Statistical Science in the Courtroom. MUKHOPADHYAY, P. Topics in Survey Sampling. RASHIDI, H. H. and BUEHLER, L. K. Bioinformatics Basics: Applications in Biological Science and Medicine. MUELLER, L. D. and JOSHI, A. Stability in Model Populations. DUFFY, S. W., HILL, C. and ESTEVE, J. (editors). Quantitative Methods for the Evaluation of Cancer Screening. FERNHOLZ, L. T., MORGENTHALER, S. and STAHEL, W. Statistics in Genetics and in the Environmental Sciences. RAYNER, J. C. W. and BEST, D. J. A Contingency Table Approach to Nonparametric Testing. GOLYANDINA, N., NEKRUTKIN, V. and ZHIGLJAVSKY, A. Analysis of Time Series Structure: SSA and Related Techniques. MARI, D. D. and KOTZ, S. Correlation and Dependence. SALSBURG, D. The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century. W. FAHRMEIR, L. and TUTZ, G. Multivariate Statistical Modelling Based on Generalized Linear Models, 2nd edition. WILCOX, R. R. Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy. DAVID, H. A. and EDWARDS, A. W. F. Annotated Readings in the History of Statistics. ELLIOTT, P., WAKEFIELD, J. C., BEST, N. G. and BRIGGS, D. J. (editors). Spatial Epidemiology: Methods and Applications. Brief reports by the editor BARNDORFF‐NIELSEN, O. E., MIKOSCH, T. and RESNICK, S. I. (editors) LéAvy Processes: Theory and Applications. MACARTHUR, R. H. and WILSON, E. O. The Theory of Island Biogeography. ROGERS, L. Sexing the Brain.     

A Primer of Ecology with R by STEVENS,M. H. H.

A Primer of Ecology with R (M. H. H. Stevens) Péter Sólymos Handbook on Analyzing Human Genetic Data: Computational Approaches and Software (S. Lin and H. Zhao, Editors) Peter M. Visscher From Finite Sample to Asymptotic Methods in Statistics (P. K. Sen, J. M. Singer, and A. C. Pedroso de Lima) Miodrag Lovric Dynamic Linear Models with R (G. Petris, S. Petrone, and P. Campagnoli) Helio S. Migon Functional Data Analysis with R and Matlab (J. O. Ramsay, G. Hooker, and S. Graves) Hervé Cardot Continuous Bivariate Distributions, 2nd edition (N. Balakrishnan and C.‐D. Lai) Márcia D'Elia Branco Brief Reports by the Editor The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition. (T. Hastie, R. Tibshirani, and J. Friedman) Gene Expression Studies Using Affymetrix Microarrays (H. Göhlmann and W. Talloen)     

Book Reviews

Visualizing Statistical Models and Concepts (R. W. Farebrother) C. B. Borkowf Statistical Consulting (J. Cabrera and A. McDougall) A. Cowling Data Monitoring Committees in Clinical Trials: A Practical Perspective (S. S. Ellenberg, T. R. Fleming, and D. L. DeMets) B. Freidlin Combined Survey Sampling Inference: Weighing Basu's Elephants (K. Brewer) Y. G. Berger Visualising Categorical Data (M. Friendly) R. J. Marshall Estimating Animal Abundance: Closed Populations (D. L. Borchers, S. T. Buckland, and W. Zucchini) A. Chao Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics (D. Sorensen and D. Gianola) J. Whittaker Block Designs: A Randomization Approach, Volumes I and II (T. Caliński and S. Kageyama) G. M. Clarke Computational Statistics Handbook with Matlab (W. L. Martinez and A. R. Martinez) P. K. Dunn Spatial Cluster Modelling (A. B. Lawson and D. G. T. Denison, eds.) D. Allard Elements of Computational Statistics (J. E. Gentle) D. M. Smith Brief Reports by the Editor A First Course in Linear Model Theory (N. Ravishanker and D. K. Dey) Statistical Inference, 2nd edition (P. H. Garthwaite, I. T. Jolliffe, and B. Jones) Statistical Analysis of Designed Experiments, 2nd edition (H. Toutenburg) Logistic Regression: A Self‐Learning Text, 2nd edition (D. G. Kleinbaum and M. Klein) American Series in Mathematical and Management Sciences, Volume 47. USA‐II, Forum for Interdisciplinary Mathematics Proceedings on Statistics, Combinatorics, and Related Areas, Volume II of Proceedings of the University of South Alabama (U.S.A.) Conference (Mobile, Alabama, U.S.A., December 1999) (S. N. Mishra, ed.)     

Detection of abnormal fermentations in wine process by multivariate statistics and pattern recognition techniques

Three multivariate statistical techniques (Multiway Principal Component Analysis, Multiway Partial Least Squares, and Stepwise Linear Discriminant Analysis) and one artificial intelligence method (Artificial Neural Networks) were evaluated to detect and predict early abnormal behaviors of wine fermentations. The techniques were tested with data of thirty-two variables at different stages of fermentation from industrial wine fermentations of Cabernet Sauvignon. All the techniques studied considered a pre-treatment to obtain a homogeneous space and reduce the overfitting. The results were encouraging; it was possible to classify at 72h 100% of the fermentation correctly with three variables using Multiway Partial Least Squares and Artificial Neural Networks. Additional and complementary results were obtained with Stepwise Linear Discriminant Analysis, which found that ethanol, sugars and density measurements are able to discriminate abnormal behavior.     

Statistical Analysis of Environmental Space-Time Processes edited by N. D. Le and J. V. Zidek

Statistical Analysis of Environmental Space‐Time Processes (N. D. Le and J. V. Zidek) Jon Wakefield Computer‐Intensive Methods of Data Analysis in Biology (D. A. Roff) A. W. Kemp Randomization, Bootstrap, and Monte Carlo Methods in Biology (3rd Edition) (B. F. J. Manly) A. W. Kemp Applied Mixed Models in Medicine, 2nd edition (H. Brown and R. Prescott) Matthew J. Gurka Stochastic Modeling for Systems Biology (D. J. Wilkinson) Olaf Wolkenhauer Knowledge Discovery in Proteomics (I. Jurisica and D. Wigle) Zhen Zhang Computational Genome Analysis: An Introduction (R. C. Deonier, S. Tavaré, and M. S. Waterman) Marc Suling Stochastic Orders (M. Shaked and J. G. Shantikumar) Subhash Kochar Brief Reports by the Editor Measurement Error in Nonlinear Models: A Modern Perspective, 2nd edition (R. J. Carroll, D. Ruppert, L. A. Stefansky, and C. M. Crainiceanu) Basic Statistics and Pharmaceutical Statistical Applications, 2nd edition (J. E. De Muth) A Handbook of Statistical Analyses Using Stata, 4th edition (S. Rabe‐Hesketh and B. S. Everitt) Introduction to Randomized Controlled Clinical Trials, 2nd edition (J. N. S. Matthews) Survival and Event History Analysis (P. K. Andersen and N. Keiding, editors) A Pocket Guide to Epidemiology (D. G. Kleinbaum, K. M. Sullivan, and N. D. Barker)     

Introduction to General and Generalized Linear Models by MADSEN,H. and TYREGOD,P.

Introduction to General and Generalized Linear Models (H. Madsen and P. Tyregod) Clarice Demétrio Sample Sizes for Clinical Trials (S. A. Julious) Janet Wittes Learning and Inference in Computational Systems Biology. (N. D. Lawrence, M. Girolami, M. Rattray and G. Sanguinetti) Ernst Wit Brief Reports by the Editor Applied Probability, 2nd edition. (K. Lange) Regression Estimators: A Comparative Study, 2nd edition. (M. H. J. Gruber) Common Statistical Methods for Clinical Research with SAS Examples, 3rd edition. (G. A. Walker and J. Shostak)     

BIOMOD – optimizing predictions of species distributions and projecting potential future shifts under global change

A new computation framework (BIOMOD: BIOdiversity MODelling) is presented, which aims to maximize the predictive accuracy of current species distributions and the reliability of future potential distributions using different types of statistical modelling methods. BIOMOD capitalizes on the different techniques used in static modelling to provide spatial predictions. It computes, for each species and in the same package, the four most widely used modelling techniques in species predictions, namely Generalized Linear Models (GLM), Generalized Additive Models (GAM), Classification and Regression Tree analysis (CART) and Artificial Neural Networks (ANN). BIOMOD was applied to 61 species of trees in Europe using climatic quantities as explanatory variables of current distributions. On average, all the different modelling methods yielded very good agreement between observed and predicted distributions. However, the relative performance of different techniques was idiosyncratic across species, suggesting that the most accurate model varies between species. The results of this evaluation also highlight that slight differences between current predictions from different modelling techniques are exacerbated in future projections. Therefore, it is difficult to assess the reliability of alternative projections without validation techniques or expert opinion. It is concluded that rather than using a single modelling technique to predict the distribution of several species, it would be more reliable to use a framework assessing different models for each species and selecting the most accurate one using both evaluation methods and expert knowledge.     

Growth, seed development and genetic analysis in wild type and Def mutant of Pisum sativum L

Background

Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test.

Results

Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5.

Conclusions

When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.     

Fuzzy regression modeling for tool performance prediction and degradation detection

In this paper, the viability of using Fuzzy-Rule-Based Regression Modeling (FRM) algorithm for tool performance and degradation detection is investigated. The FRM is developed based on a multi-layered fuzzy-rule-based hybrid system with Multiple Regression Models (MRM) embedded into a fuzzy logic inference engine that employs Self Organizing Maps (SOM) for clustering. The FRM converts a complex nonlinear problem to a simplified linear format in order to further increase the accuracy in prediction and rate of convergence. The efficacy of the proposed FRM is tested through a case study - namely to predict the remaining useful life of a ball nose milling cutter during a dry machining process of hardened tool steel with a hardness of 52-54 HRc. A comparative study is further made between four predictive models using the same set of experimental data. It is shown that the FRM is superior as compared with conventional MRM, Back Propagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in terms of prediction accuracy and learning speed.     

用组合模型综合比较的方法分析气候变化对朱鹮潜在生境的影响

气候变化的不确定性和物种与环境关系的不确定性使气候变化生物学的研究充满变数。为了降低不确定性,人们开始用组合模型综合比较的方法研究物种对气候变化的响应。以朱鹮(Nipponia nippon)为研究对象,介绍组合模型综合比较方法的特点。朱鹮曾经高度濒危,目前种群大小在迅速恢复中;然而其分布区依旧狭小,气候变化可能是朱鹮面临的新威胁。应用BIOMOD模型中的9种模型,选择了每年的最低温和最高温、温度的季节性变异、每年的总降水量和降水的季节性变异共5个气候因子,依据WorldClim气候数据的CGCM2气候模型的A2a排放情形,计算了朱鹮当前(1950—2000年)的适宜生境和2020年、2050年、2080年3个阶段的潜在生境范围。结果表明朱鹮潜在生境将逐渐北移,生境中心脱离现在的保护区。因此,制定朱鹮的长期保护策略是必要的。9个模型在预测结果上、变量权重上和拟合优度的指标上都有差异,反映了模型本身的不确定性。气候变化的生物学效应比较复杂,应用多个模型进行综合比较,可以尽可能地减少模型所导致的误差。     

A Multiscale Approach Indicates a Severe Reduction in Atlantic Forest Wetlands and Highlights that S?o Paulo Marsh Antwren Is on the Brink of Extinction

Over the last 200 years the wetlands of the Upper Tietê and Upper Paraíba do Sul basins, in the southeastern Atlantic Forest, Brazil, have been almost-completely transformed by urbanization, agriculture and mining. Endemic to these river basins, the São Paulo Marsh Antwren (Formicivora paludicola) survived these impacts, but remained unknown to science until its discovery in 2005. Its population status was cause for immediate concern. In order to understand the factors imperiling the species, and provide guidelines for its conservation, we investigated both the species’ distribution and the distribution of areas of suitable habitat using a multiscale approach encompassing species distribution modeling, fieldwork surveys and occupancy models. Of six species distribution models methods used (Generalized Linear Models, Generalized Additive Models, Multivariate Adaptive Regression Splines, Classification Tree Analysis, Artificial Neural Networks and Random Forest), Random Forest showed the best fit and was utilized to guide field validation. After surveying 59 sites, our results indicated that Formicivora paludicola occurred in only 13 sites, having narrow habitat specificity, and restricted habitat availability. Additionally, historic maps, distribution models and satellite imagery showed that human occupation has resulted in a loss of more than 346 km2 of suitable habitat for this species since the early twentieth century, so that it now only occupies a severely fragmented area (area of occupancy) of 1.42 km2, and it should be considered Critically Endangered according to IUCN criteria. Furthermore, averaged occupancy models showed that marshes with lower cattail (Typha dominguensis) densities have higher probabilities of being occupied. Thus, these areas should be prioritized in future conservation efforts to protect the species, and to restore a portion of Atlantic Forest wetlands, in times of unprecedented regional water supply problems.