Muutke küpsiste eelistusi

E-raamat: Computational Statistics in the Earth Sciences: With Applications in MATLAB

(Woods Hole Oceanographic Institution, Massachusetts)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 19-Oct-2017
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108514941
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 86,44 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Computational Statistics in the Earth Sciences: With Applications in MATLABSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 19-Oct-2017
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108514941
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

Based on a course taught by the author, this book combines the theoretical underpinnings of statistics with the practical analysis of Earth sciences data using MATLAB. The book is organized to introduce the underlying concepts, and then extends these to the data, covering methods that are most applicable to Earth sciences. Topics include classical parametric estimation and hypothesis testing, and more advanced least squares-based, nonparametric, and resampling estimators. Multivariate data analysis, not often encountered in introductory texts, is presented later in the book, and compositional data is treated at the end. Datasets and bespoke MATLAB scripts used in the book are available online, as well as additional datasets and suggested questions for use by instructors. Aimed at entering graduate students and practicing researchers in the Earth and ocean sciences, this book is ideal for those who want to learn how to analyse data using MATLAB in a statistically-rigorous manner.

Based on a course taught by the author, this book combines theoretical underpinnings of statistics with practical analysis of Earth sciences data using MATLAB. Datasets and bespoke MATLAB scripts are available online, as well as questions for use by instructors. This is an ideal text for advanced undergraduate and graduate students.

Arvustused

'One of the main strengths of this book is the combination of mathematical rigor with extensive examples, allowing readers to work through case studies to better understand the concepts presented. The tool used for this purpose is MATLAB, which is widely used in the earth science community. Examples are drawn from geophysics, astrophysics, and anthropology (among others). Both the scripts and the data examples used in the book are available for download from the publisher's website. This book is an ideal guide for graduate students seeking a comprehensive and rigorous understanding of statistical methods in earth sciences. For the more mature earth scientist (and I include myself in that number), it provides a useful reference to widely used statistical concepts that many of us regularly encounter.' Lucy MacGregor, The Leading Edge ' this book will be a welcome and invaluable addition to any earth scientist's library.' Sven Treitel, The Leading Edge

Muu info

This book combines theoretical underpinnings of statistics with practical analysis of Earth sciences data using MATLAB. Supplementary resources are available online.
Preface xi
1 Probability Concepts
1(17)
1.1 Fundamental Concepts and Motivating Example
1(3)
1.2 Probability Philosophies
4(1)
1.3 Set Theory
5(3)
1.4 Definition of Probability
8(2)
1.5 Finite Sample Spaces
10(3)
1.5.1 Simple Sample Spaces
10(1)
1.5.2 Permutations
11(1)
1.5.3 Combinations
12(1)
1.6 Probability of a Union of Events
13(1)
1.7 Conditional Probability
14(1)
1.8 Independent Events
15(1)
1.9 Bayes' Theorem
16(2)
2 Statistical Concepts
18(30)
2.1 Overview
18(1)
2.2 The Probability Density Function
18(5)
2.2.1 Discrete Distributions
18(2)
2.2.2 Continuous Distributions
20(2)
2.2.3 Mixed Distributions
22(1)
2.3 The Cumulative Distribution and Quantile Functions
23(2)
2.4 The Characteristic Function
25(2)
2.5 Bivariate Distributions
27(1)
2.6 Independent and Exchangeable Random Variables
28(2)
2.7 Conditional Probability Distributions
30(2)
2.8 Functions of a Random Variable
32(2)
2.9 Functions of Two or More Random Variables
34(2)
2.10 Measures of Location
36(3)
2.11 Measures of Dispersion
39(2)
2.12 Measures of Shape
41(1)
2.13 Measures of Direction
42(2)
2.14 Measures of Association
44(1)
2.15 Conditional Expected Value and Variance
44(1)
2.16 Probability Inequalities
45(1)
2.17 Convergence of Random Variables
46(2)
3 Statistical Distributions
48(38)
3.1 Overview
48(1)
3.2 MATLAB Support for Distributions
48(1)
3.3 Discrete Distributions
49(13)
3.3.1 Bernoulli Distribution
49(1)
3.3.2 Binomial Distribution
50(3)
3.3.3 Negative Binomial Distribution
53(1)
3.3.4 Multinomial Distribution
54(2)
3.3.5 Hypergeometric Distribution
56(3)
3.3.6 Poisson Distribution
59(3)
3.4 Continuous Distributions
62(24)
3.4.1 Normal or Gaussian Distribution
62(3)
3.4.2 Stable Distributions
65(2)
3.4.3 Rayleigh Distribution
67(2)
3.4.4 Lognormal Distribution
69(1)
3.4.5 Gamma Distribution
70(2)
3.4.6 Exponential Distribution
72(2)
3.4.7 Weibull Distribution
74(2)
3.4.8 Beta Distribution
76(2)
3.4.9 Generalized Extreme Value Distribution
78(2)
3.4.10 Bivariate Gaussian Distribution
80(1)
3.4.11 Directional Distributions
81(5)
4 Characterization of Data
86(56)
4.1 Overview
86(1)
4.2 Estimators of Location
86(5)
4.3 Estimators of Dispersion
91(2)
4.4 Estimators of Shape
93(2)
4.5 Estimators of Direction
95(5)
4.6 Estimators of Association
100(1)
4.7 Limit Theorems
101(5)
4.7.1 The Laws of Large Numbers
101(1)
4.7.2 Classic Central Limit Theorems
102(2)
4.7.3 Other Central Limit Theorems
104(1)
4.7.4 The Delta Method
105(1)
4.8 Exploratory Data Analysis Tools
106(16)
4.8.1 The Probability Integral Transform
106(1)
4.8.2 The Histogram and Empirical CDF
107(4)
4.8.3 Kernel Density Estimators
111(4)
4.8.4 The Percent-Percent and Quantile-Quantile Plots
115(5)
4.8.5 Simulation
120(2)
4.9 Sampling Distributions
122(13)
4.9.1 Chi Square Distributions
122(3)
4.9.2 Student's t Distributions
125(3)
4.9.3 The F Distributions
128(2)
4.9.4 The Correlation Coefficient
130(5)
4.10 Distributions for Order Statistics
135(5)
4.10.1 Distribution of a Single Order Statistic
135(2)
4.10.2 Distribution of the Sample Median
137(1)
4.10.3 Joint Distribution of a Pair of Order Statistics
138(1)
4.10.4 Distribution of the Interquartile Range
138(2)
4.11 Joint Distribution of the Sample Mean and Sample Variance
140(2)
5 Point, Interval, and Ratio Estimators
142(27)
5.1 Overview
142(1)
5.2 Optimal Estimators
142(12)
5.2.1 Consistency
142(1)
5.2.2 Unbiased Estimators
143(1)
5.2.3 Efficiency and the Cramer-Rao Lower Bound
144(3)
5.2.4 Robustness
147(1)
5.2.5 Sufficient Statistics
148(4)
5.2.6 Statistical Decision Theory
152(2)
5.3 Point Estimation: Method of Moments
154(1)
5.4 Point Estimation: Maximum Likelihood Estimator
155(5)
5.5 Interval Estimation: Confidence and Tolerance Intervals
160(6)
5.6 Ratio Estimators
166(3)
6 Hypothesis Testing
169(45)
6.1 Introduction
169(2)
6.2 Theory of Hypothesis Tests I
171(6)
6.3 Parametric Hypothesis Tests
177(18)
6.3.1 The z Test
177(1)
6.3.2 The t Tests
178(8)
6.3.3 The X2 Test
186(2)
6.3.4 The F Test
188(1)
6.3.5 Bartlett's M Test for Homogeneity of Variance
189(1)
6.3.6 The Correlation Coefficient
190(2)
6.3.7 Analysis of Variance
192(2)
6.3.8 Sample Size and Power
194(1)
6.4 Hypothesis Tests and Confidence Intervals
195(1)
6.5 Theory of Hypothesis Tests II
196(14)
6.5.1 Likelihood Ratio Tests for Simple Hypotheses
197(1)
6.5.2 Uniformly Most Powerful Tests
198(2)
6.5.3 Likelihood Ratio Tests for Composite Hypotheses
200(7)
6.5.4 The Wald Test
207(1)
6.5.5 The Score Test
208(2)
6.6 Multiple Hypothesis Tests
210(4)
7 Nonparametric Methods
214(33)
7.1 Overview
214(1)
7.2 Goodness-of-Fit Tests
214(17)
7.2.1 Likelihood Ratio Test for the Multinomial Distribution
214(5)
7.2.2 Pearson's x2 Test for Goodness-of-Fit
219(3)
7.2.3 Kolmogorov-Smirnov Test
222(6)
7.2.4 Cramer-von Mises Tests
228(2)
7.2.5 Jarque-Bera Test
230(1)
7.3 Tests Based on Ranks
231(14)
7.3.1 Properties of Ranks
231(1)
7.3.2 Sign Test
232(3)
7.3.3 Signed Rank Test
235(2)
7.3.4 Rank Sum Test
237(3)
7.3.5 Ansari-Bradley Test
240(1)
7.3.6 Spearman Rank Correlation Test
241(1)
7.3.7 Kendall's Tau
242(1)
7.3.8 Nonparametric ANOVA
243(2)
7.4 Meta-analysis
245(2)
8 Resampling Methods
247(34)
8.1 Overview
247(1)
8.2 The Bootstrap
247(20)
8.2.1 The Bootstrap Distribution
247(4)
8.2.2 Bootstrap Parameter Estimation
251(4)
8.2.3 Bootstrap Confidence Intervals
255(4)
8.2.4 Bootstrap Hypothesis Tests
259(6)
8.2.5 Bias Correction for Goodness-of-Fit Tests
265(2)
8.3 Permutation Tests
267(10)
8.3.1 Principles
267(1)
8.3.2 One-Sample Test for a Location Parameter
268(2)
8.3.3 Two-Sample Test for a Location Parameter
270(4)
8.3.4 Two-Sample Test for Paired Data
274(1)
8.3.5 Two-Sample Test for Dispersion
275(2)
8.4 The Jackknife
277(4)
9 Linear Regression
281(63)
9.1 Motivating Example
281(2)
9.2 Statistical Basis for Linear Regression
283(3)
9.3 Numerical Considerations
286(3)
9.4 Statistical Inference in Linear Regression
289(6)
9.4.1 Analysis of Variance
289(2)
9.4.2 Hypothesis Testing on the Regression Estimates
291(1)
9.4.3 Confidence Intervals
292(2)
9.4.4 The Runs and Durbin-Watson Tests
294(1)
9.5 Linear Regression in Practice
295(21)
9.5.1 Assessing the Results
295(3)
9.5.2 Examples
298(18)
9.6 Robust and Bounded Influence Regression
316(19)
9.6.1 Robust Estimators
317(10)
9.6.2 Bounded Influence Estimators
327(8)
9.7 Advanced Linear Regression
335(9)
9.7.1 Errors in Variables
335(1)
9.7.2 Shrinkage Estimators
336(4)
9.7.3 Logistic Regression
340(4)
10 Multivariate Statistics
344(47)
10.1 Concepts and Notation
344(2)
10.2 The Multivariate Gaussian Distribution
346(4)
10.2.1 Derivation of the Multivariate Gaussian Distribution
346(1)
10.2.2 Properties of the MV Gaussian Distribution
347(1)
10.2.3 The Sample Mean Vector and Sample Covariance Matrix
348(1)
10.2.4 The Complex Multivariate Gaussian Distribution
349(1)
10.3 Hotelling's T2 Tests
350(4)
10.4 Multivariate Analysis of Variance
354(8)
10.5 Hypothesis Tests on the Covariance Matrix
362(4)
10.5.1 Sphericity Test
363(1)
10.5.2 Comparing Covariance Matrices
364(1)
10.5.3 Test of Independence
365(1)
10.6 Multivariate Regression
366(5)
10.7 Canonical Correlation
371(2)
10.8 Empirical Orthogonal Functions
373(18)
10.8.1 Theory
374(3)
10.8.2 Choosing the Number of Eofs
377(1)
10.8.3 Example
378(7)
10.8.4 Empirical Orthogonal Function Regression
385(6)
11 Compositional Data
391(38)
11.1 Introduction
392(1)
11.2 Statistical Concepts for Compositions
392(13)
11.2.1 Definitions and Principles
392(3)
11.2.2 Compositional Geometry
395(5)
11.2.3 Compositional Transformations
400(5)
11.3 Exploratory Compositional Data Analysis
405(24)
Appendix 11A MATLAB Functions to Produce Ternary Diagrams 429(6)
References 435(9)
Index 444
Alan D. Chave is a Senior Scientist at Woods Hole Oceanographic Institution (WHOI), Massachusetts. He has been a Chartered Statistician since 2003, and has taught a graduate-level course in statistics in the MIT/WHOI Joint Program for twenty years. For over forty years, he has conducted research utilizing the magnetotelluric method, primarily in the oceans, and using electromagnetic measurements to define the barotropic water velocity. Dr Chave has also designed instrumentation for optical and chemical measurements in the ocean, and has played a leading role in the development of long-term ocean observatories worldwide. He has been an associate editor of the Journal of Geophysical Research and editor-in-chief of Reviews of Geophysics, and is the co-author of The Magnetotelluric Method (Cambridge, 2012).
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97811085149412e.html
Märksõnad: