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ISBN: 9781118593370 , 1118593375 , 9781118593363 , 1118593367 , 9781118593233 , 1118593235 , 1118422546 , 9781118422540 , 9781322007595 , 1322007594

Inhalt: "This book emphasizes the collaborative analysis of data that is used to collect increments of time or space. Written at a readily accessible level, but with the necessary theory in mind, the author uses frequency- and time-domain and trigonometric regression as themes throughout the book. The content includes modern topics such as wavelets, Fourier series, and Akaike's Information Criterion (AIC), which is not typical of current-day "classics." Applications to a variety of scientific fields are showcased. Exercise sets are well crafted with the express intent of supporting pedagogy through recognition and repetition. R subroutines are employed as the software and graphics tool of choice. Brevity is a key component to the retention of the subject matter. The book presumes knowledge of linear algebra, probability, data analysis, and basic computer programming"--

Inhalt: "This book emphasizes the collaborative analysis of data that is used to collect increments of time or space. Written at a readily accessible level, but with the necessary theory in mind, the author uses frequency- and time-domain and trigonometric regression as themes throughout the book"--

Anmerkung: R Basics -- , Getting Started, -- , Special R Conventions, -- , Common Structures, -- , Common Functions, -- , Time Series Functions, -- , Importing Data, -- , Exercises, -- , Review of Regression and More About R -- , Goals of this Chapter, -- , The Simple(ST) Regression Model, -- , Ordinary Least Squares, -- , Properties of OLS Estimates, -- , Matrix Representation of the Problem, -- , Simulating the Data from a Model and Estimating the Model Parameters in R, -- , Simulating Data, -- , Estimating the Model Parameters in R, -- , Basic Inference for the Model, -- , Residuals Analysis[2014]What Can Go Wrong, -- , Matrix Manipulation in R, -- , Introduction, -- , OLS the Hard Way, -- , Some Other Matrix Commands, -- , Exercises, -- , The Modeling Approach Taken in this Book and Some Examples of Typical Serially Correlated Data -- , Signal and Noise, -- , Time Series Data, -- , Simple Regression in the Framework, -- , Real Data and Simulated Data, -- , The Diversity of Time Series Data, -- , Getting Data Into R, -- , Overview, -- , The Diskette and the scan() and ts() Functions[2014]New York City Temperatures, -- , The Diskette and the read.table() Function[2014]The Semmelweis Data, -- , Cut and Paste Data to a Text Editor, -- , Exercises, -- , Some Comments on Assumptions -- , Introduction, -- , The Normality Assumption, -- , Right Skew, -- , Left Skew, -- , Heavy Tails, -- , Equal Variance, -- , Two-Sample t-Test, -- , Regression, -- , Independence, -- , Power of Logarithmic Transformations Illustrated, -- , Summary, -- , Exercises, -- , The Autocorrelation Function And AR(1), AR(2) Models -- , Standard Models[2014]What are the Alternatives to White Noise?, -- , Autocovariance and Autocorrelation, -- , Stationarity, -- , A Note About Conditions, -- , Properties of Autocovariance, -- , White Noise, -- , Estimation of the Autocovariance and Autocorrelation, -- , The acf() Function in R, -- , Background, -- , The Basic Code for Estimating the Autocovariance, -- , The First Alternative to White Noise: Autoregressive Errors[2014]AR(1), AR(2), -- , Definition of the AR(1) and AR(2) Models, -- , Some Preliminary Facts, -- , The AR(1) Model Autocorrelation and Autocovariance, -- , Using Correlation and Scatterplots to Illustrate the AR(1) Model, -- , The AR(2) Model Autocorrelation and Autocovariance, -- , Simulating Data for AR(m) Models, -- , Examples of Stable and Unstable AR(1) Models, -- , Examples of Stable and Unstable AR(2) Models, -- , Exercises, -- , The Moving Average Models MA(1) And MA(2) -- , The Moving Average Model, -- , The Autocorrelation for MA(1) Models, -- , A Duality Between MA(l) And AR(m) Models, -- , The Autocorrelation for MA(2) Models, -- , Simulated Examples of the MA(1) Model, -- , Simulated Examples of the MA(2) Model, -- , AR(m) and MA(l) model acf() Plots, -- , Exercises, -- , Review of Transcendental Functions and Complex Numbers -- , Background, -- , Complex Arithmetic, -- , The Number i, -- , Complex Conjugates, -- , The Magnitude of a Complex Number, -- , Some Important Series, -- , The Geometric and Some Transcendental Series, -- , A Rationale for Euler's Formula, -- , Useful Facts About Periodic Transcendental Functions, -- , Exercises, -- , The Power Spectrum and the Periodogram -- , Introduction, -- , A Definition and a Simplified Form for p(f), -- , Inverting p(f) to Recover the Ck Values, -- , The Power Spectrum for Some Familiar Models, -- , White Noise, -- , The Spectrum for AR(1) Models, -- , The Spectrum for AR(2) Models, -- , The Periodogram, a Closer Look, -- , Why is the Periodogram Useful?, -- , Some Naive Code for a Periodogram, -- , An Example[2014]The Sunspot Data, -- , The Function spec.pgram() in R, -- , Exercises, -- , Smoothers, The Bias-Variance Tradeoff, and the Smoothed Periodogram -- , Why is Smoothing Required?, -- , Smoothing, Bias, and Variance, -- , Smoothers Used in R, -- , The R Function lowess(), -- , The R Function smooth.spline(), -- , Kernel Smoothers in spec.pgram(), -- , Smoothing the Periodogram for a Series With a Known and Unknown Period, -- , Period Known, -- , Period Unknown, -- , Summary, -- , Exercises, -- , A Regression Model for Periodic Data -- , The Model, , An Example: The NYC Temperature Data, -- , Fitting a Periodic Function, -- , An Outlier, -- , Refitting the Model with the Outlier Corrected, -- , Complications 1: CO2 Data, -- , Complications 2: Sunspot Numbers, -- , Complications 3: Accidental Deaths, -- , Summary, -- , Exercises, -- , Model Selection and Cross-Validation -- , Background, -- , Hypothesis Tests in Simple Regression, -- , A More General Setting for Likelihood Ratio Tests, -- , A Subtlety Different Situation, -- , Information Criteria, -- , Cross-validation (Data Splitting): NYC Temperatures, -- , Explained Variation, R2, -- , Data Splitting, -- , Leave-One-Out Cross-Validation, -- , AIC as Leave-One-Out Cross-Validation, -- , Summary, -- , Exercises, -- , Fitting Fourier series -- , Introduction: More Complex Periodic Models, -- , More Complex Periodic Behavior: Accidental Deaths, -- , Fourier Series Structure, -- , R Code for Fitting Large Fourier Series, -- , Model Selection with AIC, -- , Model Selection with Likelihood Ratio Tests, -- , Data Splitting, -- , Accidental Deaths[2014]Some Comment on Periodic Data, -- , The Boise River Flow data, -- , The Data, -- , Model Selection with AIC, -- , Data Splitting, -- , The Residuals, -- , Where Do We Go from Here?, -- , Exercises, -- , Adjusting for AR(1) Correlation in Complex Models -- , Introduction, -- , The Two-Sample t-Test[2014]UNCUT and Patch-Cut Forest, -- , The Sleuth Data and the Question of Interest, -- , A Simple Adjustment for t-Tests When the Residuals Are AR(1), -- , A Simulation Example, -- , Analysis of the Sleuth Data, -- , The Second Sleuth Case[2014]Global Warming, A Simple Regression, -- , The Data and the Question, -- , Filtering to Produce (Quasi- )Independent Observations, -- , Simulated Example[2014]Regression, -- , Analysis of the Regression Case, -- , The Filtering Approach for the Logging Case, -- , A Few Comments on Filtering, -- , The Semmelweis Intervention, -- , The Data, -- , Why Serial Correlation?, -- , How This Data Differs from the Patch/Uncut Case, -- , Filtered Analysis, -- , Transformations and Inference, -- , The NYC Temperatures (Adjusted), -- , The Data and Prediction Intervals, -- , The AR(1) Prediction Model, -- , A Simulation to Evaluate These Formulas, -- , Application to NYC Data, -- , The Boise River Flow Data: Model Selection With Filtering, -- , The Revised Model Selection Problem, -- , Comments on R2 and R2pred' -- , Model Selection After Filtering with a Matrix, -- , Implications of AR(1) Adjustments and the "Skip" Method, -- , Adjustments for AR(1) Autocorrelation, -- , Impact of Serial Correlation on p-Values, -- , The "skip" Method, -- , Summary, -- , Exercises, -- , The Backshift Operator, the Impulse Response Function, and General ARMA Models -- , The General ARMA Model, -- , The Mathematical Formulation, -- , The arima.sim() Function in R Revisited, -- , Examples of ARMA(m, l) Models, -- , The Backshift (Shift, Lag) Operator, -- , Definition of B, -- , The Stationary Conditions for a General AR(m) Model, -- , ARMA(m, l) Models and the Backshift Operator, -- , More Examples of ARMA(m, l) Models, -- , The Impulse Response Operator[2014]Intuition, -- , Impulse Response Operator, g(B)[2014]Computation, -- , Definition of g(B), -- , Computing the Coefficients, -- , Plotting an Impulse Response Function, -- , Interpretation and Utility of the Impulse Response Function, -- , Exercises, -- , The Yule[2014]Walker Equations and the Partial Autocorrelation Function -- , Background, -- , Autocovariance of an ARMA(m, /) Model, -- , A Preliminary Result, -- , The Autocovariance Function for ARMA(m, /) Models, -- , AR(m) and the Yule[2014]Walker Equations, -- , The Equations, -- , The R Function aryw() with an AR(3) Example, -- , Information Criteria-Based Model Selection Using aryw(), -- , The Partial Autocorrelation Plot, -- , A Sequence of Hypothesis Tests, -- , The pacf() Function[2014]Hypothesis Tests Presented in a Plot, -- , The Spectrum For Arma Processes, -- , Summary, -- , Exercises, -- , Modeling Philosophy and Complete Examples -- , Modeling Overview, -- , The Algorithm, , The Underlying Assumption, -- , An Example Using an AR(m) Filter to Model MA(3), -- , Generalizing the "Skip" Method, -- , A Complex Periodic Model[2014]Monthly River Flows, Fumas 1931-1978, -- , The Data, -- , A Saturated Model, -- , Building an AR(m) Filtering Matrix, -- , Model Selection, -- , Predictions and Prediction Intervals for an AR(3) Model, -- , Data Splitting, -- , Model Selection Based on a Validation Set, -- , A Modeling Example[2014]Trend and Periodicity: CO2 Levels at Mauna Lau, -- , The Saturated Model and Filter, -- , Model Selection, -- , How Well Does the Model Fit the Data?, -- , Modeling Periodicity with a Possible Intervention[2014]Two Examples, -- , The General Structure, -- , Directory Assistance, -- , Ozone Levels in Los Angeles, -- , Interpretation and Utility of the Impulse Response Function, -- , Exercises, -- , The Yule[2014]Walker Equations and the Partial Autocorrelation Function -- , Background, -- , Autocovariance of an ARMA(m, l) Model, -- , A Preliminary Result, -- , The Autocovariance Function for ARMA(m, /) Models, -- , AR(m) and the Yule[2014]Walker Equations, -- , The Equations, -- , The R Function ar.yw() with an AR(3) Example, -- , Information Criteria-Based Model Selection Using ar.yw(), -- , The Partial Autocorrelation Plot, -- , A Sequence of Hypothesis Tests, -- , The pacf() Function[2014]Hypothesis Tests Presented in a Plot, -- , The Spectrum For Arma Processes, -- , Summary, -- , Exercises, -- , Modeling Philosophy and Complete Examples -- , Modeling Overview, -- , The Algorithm, -- , The Underlying Assumption, -- , An Example Using an AR(m) Filter to Model MA(3), -- , Generalizing the "Skip" Method, -- , A Complex Periodic Model[2014]Monthly River Flows, Fumas 1931-1978, -- , The Data, -- , A Saturated Model, -- , Building an AR(m) Filtering Matrix, -- , Model Selection, -- , Predictions and Prediction Intervals for an AR(3) Model, -- , Data Splitting, -- , Model Selection Based on a Validation Set, -- , A Modeling Example[2014]Trend and Periodicity: CO2 Levels at Mauna Lau, -- , The Saturated Model and Filter, -- , Model Selection, -- , How Well Does the Model Fit the Data?, -- , Modeling Periodicity with a Possible Intervention[2014]Two Examples, -- , The General Structure, -- , Directory Assistance, -- , Ozone Levels in Los Angeles, -- , Periodic Models: Monthly, Weekly, and Daily Averages, -- , Summary, -- , Exercises, -- , Wolf's Sunspot Number Data -- , Background, -- , Unknown Period -〉 Nonlinear Model, -- , The Function nls() in R, -- , Determining the Period, -- , Instability in the Mean, Amplitude, and Period, -- , Data Splitting for Prediction, -- , The Approach, -- , Step 1-Fitting One Step Ahead, -- , The AR Correction, -- , Putting it All Together, -- , Model Selection, -- , Predictions Two Steps Ahead, -- , Summary, -- , Exercises, -- , An Analysis of Some Prostate and Breast Cancer Data -- , Background, -- , The First Data Set, -- , The Second Data Set, -- , Background and Questions, -- , Outline of the Statistical Analysis, -- , Looking at the Data, -- , Examining the Residuals for AR(m) Structure, -- , Regression Analysis with Filtered Data, -- , Exercises, -- , Christopher Tennant/Ben Crosby Watershed Data -- , Background and Question, -- , Looking at the Data and Fitting Fourier Series, -- , The Structure of the Data, -- , Fourier Series Fits to the Data, -- , Connecting Patterns in Data to Physical Processes, -- , Averaging Data, -- , Results, -- , Exercises, -- , Vostok Ice Core Data -- , Source of the Data, -- , Background, -- , Alignment, -- , Need for Alignment, and Possible Issues Resulting from Alignment, -- , Is the Pattern in the Temperature Data Maintained?, -- , Are the Dates Closely Matched?, -- , Are the Times Equally Spaced?, -- , A Naïve Analysis, -- , A Saturated Model, -- , Model Selection, -- , The Association Between CO2 and Temperature Change, -- , A Related Simulation, -- , The Model and the Question of Interest, -- , Simulation Code in R, -- , A Model Using all of the Simulated Data, -- , A Model Using a Sample of 283 from the Simulated Data, -- , An AR(1) Model for Irregular Spacing, -- , Motivation, -- , Method, -- , Results, -- , Sensitivity Analysis, -- , A Final Analysis, Well Not Quite, -- , Summary, -- , Exercises, -- , Overview, -- , Loading a Time Series in Datamarket, -- , Respecting Datamarket Licensing Agreements, -- , Introduction, -- , PRESS, -- , Connection to Akaike's Result, -- , Normalization and R2, -- , An example, -- , Conclusion and Further Comments, -- , Introduction, -- , Newton's Method for One-Dimensional Nonlinear Optimization, -- , A Sequence of Directions, Step Sizes, and a Stopping Rule, -- , What Could Go Wrong?, -- , Generalizing the Optimization Problem, -- , What Could Go Wrong[2014]Revisited, -- , What Can be Done?

Weitere Ausg.: Print version: Derryberry, DeWayne R. Basic data analysis for time series with R. Hoboken, New Jersey : John Wiley & Sons, Inc., [2014] ISBN 9781118422540

Sprache: Englisch

Schlagwort(e): Electronic books. ; Electronic books. ; Electronic books.

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118593233

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118593233

URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118593233