Tabla de Contenidos: “…11.5.2 Wide-Sense Ergodic Processes 333 -- 11.6 Gaussian Processes 336 -- 11.7 Poisson Processes 339 -- 11.8 Summary 341 -- Problems 341 -- 12 Analysis and Processing of Random Processes 345 -- 12.1 Stochastic Continuity, Differentiation, and Integration 345 -- 12.1.1 Mean-Square Continuity 345 -- 12.1.2 Mean-Square Derivatives 346 -- 12.1.3 Mean-Square Integrals 347 -- 12.2 Power Spectral Density 347 -- 12.3 Noise 353 -- 12.3.1 White Noise 353 -- 12.4 Sampling of Random Signals 355 -- 12.5 Optimum Linear Systems 357 -- 12.5.1 Systems Maximizing Signal-to-Noise Ratio 357 -- 12.5.2 Systems Minimizing Mean-Square Error 359 -- 12.6 Summary 362 -- Problems 362 -- Bibliography 365 -- Books 365 -- Internet Websites 368 -- Answers 369 -- Index 387.…”

Tabla de Contenidos: “…Var[b] -- 4.4.3 Asymptotic Normality of the Least Squares Estimator -- 4.4.4 Asymptotic Efficiency -- 4.4.5 Linear Projections -- 4.5 Robust Estimation and Inference -- 4.5.1 Consistency of the Least Squares Estimator -- 4.5.2 A Heteroscedasticity Robust Covariance Matrix for Least Squares -- 4.5.3 Robustness to Clustering -- 4.5.4 Bootstrapped Standard Errors with Clustered Data -- 4.6 Asymptotic Distribution of a Function of b: The Delta Method -- 4.7 Interval Estimation -- 4.7.1 Forming a Confidence Interval for a Coefficient -- 4.7.2 Confidence Interval for a Linear Combination of Coefficients: the Oaxaca Decomposition -- 4.8 Prediction and Forecasting -- 4.8.1 Prediction Intervals -- 4.8.2 Predicting y when the Regression Model Describes Log y -- 4.8.3 Prediction Interval for y when the Regression Model Describes Log y -- 4.8.4 Forecasting -- 4.9 Data Problems -- 4.9.1 Multicollinearity -- 4.9.2 Principal Components -- 4.9.3 Missing Values and Data Imputation -- 4.9.4 Measurement Error -- 4.9.5 Outliers and Influential Observations -- 4.10 Summary and Conclusions -- CHAPTER 5 Hypothesis Tests and Model Selection -- 5.1 Introduction -- 5.2 Hypothesis Testing Methodology -- 5.2.1 Restrictions and Hypotheses -- 5.2.2 Nested Models -- 5.2.3 Testing Procedures -- 5.2.4 Size, Power, and Consistency of a Test -- 5.2.5 A Methodological Dilemma: Bayesian Versus Classical Testing -- 5.3 Three Approaches to Testing Hypotheses -- 5.3.1 Wald Tests Based on the Distance Measure…”

Tabla de Contenidos: “…-- 3.6.3 Nominal Interest Rates -- 3.7 Appendix: Force of Interest - An Analogy with Mortality Rates -- 3.8 Recommended Reading -- 4 Financial Mathematics (2): Miscellaneous Examples -- 4.1 Introduction -- 4.2 Writing Annuity Functions -- 4.2.1 Writing a function for an annuity certain -- 4.3 The 'presentValue' Function -- 4.4 Annuity Function -- 4.5 Bonds - Pricing and Yield Calculations -- 4.6 Bond Pricing: Non-Constant Interest Rates -- 4.7 The Effect of Future Yield Changes on Bond Prices Throughout the Term of the Bond -- 4.8 Loan Schedules -- 4.8.1 Introduction -- 4.8.2 Method 1 -- 4.8.3 Method 2 -- 4.9 Recommended Reading -- 5 Fundamental Statistics: A Selection of Key Topics -- 5.1 Introduction -- 5.2 Basic Distributions in Statistics -- 5.3 Some Useful Functions for Descriptive Statistics -- 5.3.1 Introduction -- 5.3.2 Bivariate or Higher Order Data Structure -- 5.4 Statistical Tests -- 5.4.1 Exploring for Normality or Any Other Distribution in the Data -- 5.4.2 Goodness-of-fit Testing for Fitted Distributions to Data -- 5.4.2.1 Continuous distributions -- 5.4.2.2 Discrete distributions -- 5.4.3 T-tests -- 5.4.3.1 One sample test for the mean -- 5.4.3.2 Two sample tests for the mean -- 5.4.4 F-test for Equal Variances -- 5.5 Main Principles of Maximum Likelihood Estimation -- 5.5.1 Introduction -- 5.5.2 MLE of the Exponential Distribution -- 5.5.2.1 Obtaining the MLE numerically using R -- 5.5.2.2 Obtaining the MLE analytically -- 5.5.3 Large Sample (Asymptotic) Properties of MLE…”

Tabla de Contenidos: “…2.2 Methodology -- 2.2.1 Collection of Plant Material -- 2.2.2 Qualitative Analysis of Phytochemicals -- 2.2.3 Study of In Vitro Antiurolithiatic Activity Using Titrimetry Method -- 2.2.3.1 Preparation of Calcium Oxalate -- 2.2.3.2 Preparation of Semipermeable Membrane From Eggs -- 2.2.3.3 In Vitro Antiurolithiatic Test Using Titrimetry Method -- 2.3 Result and Discussion -- 2.3.1 In Vitro Antiurolithiatic Activity Test -- 2.3.2 Analysis of Dissolved Calcium Oxalate -- 2.4 Conclusion -- References -- Chapter 3 Parkinson's Disease Detection Using Voice and Speech- Systematic Literature Review -- 3.1 Introduction -- 3.2 Research Questions -- 3.3 Method -- 3.3.1 Search Strategy -- 3.3.2 Inclusion Criteria -- 3.3.3 Subprocesses Involved in PD Detection Process -- 3.3.4 Data Sets -- 3.3.4.1 Parkinson's Data Set-UCI Machine Learning Dataset -- 3.3.4.2 PC-GITA Dataset -- 3.3.4.3 mPower Dataset -- 3.3.4.4 Mobile Device Voice Recordings (MDVR-KCL) Dataset -- 3.3.4.5 Italian Parkinson's Voice and Speech (IPVS) Dataset -- 3.3.4.6 Parkinson Speech Dataset With Multiple Types of Sound Recordings Dataset -- 3.3.4.7 Parkinson's Telemonitoring Dataset -- 3.4 Algorithms -- 3.5 Features -- 3.5.1 Acoustic Features -- 3.5.1.1 Jitter (Local, Absolute) -- 3.5.1.2 Jitter (Local) -- 3.5.1.3 Jitter (rap) -- 3.5.1.4 Jitter (ppq5) -- 3.5.1.5 Shimmer (Local) -- 3.5.1.6 Shimmer (local, dB) -- 3.5.1.7 Shimmer (apq3) -- 3.5.1.8 Shimmer (apq5) -- 3.5.2 Spectogram-Based Methods -- 3.5.2.1 MFCC -- 3.6 Conclusion -- References -- Chapter 4 Tumor Detection and Classification -- 4.1 Introduction -- 4.2 Methods Used for Detection of Tumors -- 4.3 Methods Used for Classification of Tumours -- 4.3.1 Segmentation -- 4.3.2 Region Growing Method -- 4.3.3 Seeded Region Growing Method -- 4.3.4 Unseeded Region Growing Method -- 4.3.5 .…”

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