THIRD EDITION (2022) The Applied CQRM Book Series showcases how the advanced analytics covered in the Certified in Quantitative Risk Management (CQRM) certification program can be applied to real-life business problems. In Volume I, we show how Risk Simulator and ROV BizStats can be used to perform quantitative analysis in graduate and postgraduate research. Pragmatic applications are emphasized in order to demystify the many elements inherent in quantitative analysis. A statistical black box will remain a black box if no one can understand the concepts despite its power and applicability. It is only when the black box methods become transparent, so that researchers can understand, apply, and convince others of their results, value-add, and applicability, that the approaches will receive widespread attention. This transparency is achieved through step-by-step applications of quantitative modeling as well as presenting multiple cases and discussing real-life applications. This book is targeted at those individuals who have completed the CQRM certification program but can also be used by anyone familiar with basic quantitative research methods--there is some-thing for everyone. It is also applicable for use as a second-year MBA/MS-level or introductory PhD textbook. The examples in the book assume some prior knowledge of the subject matter. Additional information on the CQRM program can be obtained at: www.iiper.org www.realoptionsvaluation.com THE BASICS Central Tendency, Spread, Skew, Kurtosis Probability, Bayes' Theorem, Trees, Combination, Permutation Classical, Standard, P-Value, CI Central Limit Theorem Type I-IV Errors, Sampling Biases >ANALYTICAL METHODS T-Tests: Equal/Unequal/Paired Variance, F-Test, Z-Test ANOVA, Blocked, Two-Way, ANCOVA, MANOVA Linear/Nonlinear Correlation Normality & Distributional Fitting: Kolmogorov-Smirnov, Chi-Square, Akaike Information Criterion, Anderson-Darling, Kuiper's, Schwarz/Bayes, Box-Cox Nonparametrics: Runs, Wilcoxon, Mann-Whitney, Lilliefors, Q-Q, D'Agostino-Pearson, Shapiro-Wilk-Royston, Kruskal-Wallis, Mood's, Cochran's Q, Friedman's Inter/Intra-Rater Reliability, Consistency, Diversity, Internal/External Validity, Predictability Cohen's Kappa, Cronbach's Alpha, Guttman's Lambda, Inter-Class Correlation, Kendall's W, Shannon-Brillouin-Simpson Diversity, Homogeneity, Grubbs Outlier, Mahalanobis, Linear & Quadratic Discriminant, Hannan-Quinn, Diebold-Mariano, Pesaran-Timmermann, Precision, Error Control Linear/Nonlinear Multivariate Regression Multicollinearity, Heteroskedasticity Structural Equation Modeling (SEM), Partial Least Squares (PLS) Endogeneity, Simultaneous Equations Methods, Two-Stage Least Squares Granger Causality, Engle-Granger >ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (DATA SCIENCE) Bagging Linear Bootstrap Bagging Nonlinear Bootstrap Classification and Regression Trees CART Custom Fit Dimension Reduction Principal Component Analysis Dimension Reduction Factor Analysis Ensemble Common Fit Ensemble Complex Fit Ensemble Time-Series Gaussian Mix & K-Means Segmentation K-Nearest Neighbors Linear Fit Model Multivariate Discriminant Analysis (Linear) Multivariate Discriminant Analysis (Quadratic) Neural Network (Cosine, Tangent, Hyperbolic) Logistic Binary Classification Normit-Probit Binary Classification Phylogenetic Trees & Hierarchical Clustering Random Forest Segmentation Clustering Support Vector Machines SVM
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