St. Xavier's University Library Search for 'se,phr:"Probability and Statistics "'

Experiments / planning, analysis, and optimization

]]> By Wu, C.f. Jeff.
New Delhi Wiley 2009 .
716p , includes index 9788126553969

Applied regression analysis

]]> By Draper, Norman R..
New Delhi Wiley 2019 .
706p , includes index 9788126531738

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An introduction to multivariate statistical analysis

]]> By Anderson, T. W..
Hoboken, N.J. : Wiley-Interscience, 2003 .
xx, 721 p. : , Preface to the Third Edition. Preface to the Second Edition. Preface to the First Edition. 1. Introduction. 2. The Multivariate Normal Distribution. 3. Estimation of the Mean Vector and the Covariance Matrix. 4. The Distributions and Uses of Sample Correlation Coefficients. 5. The Generalized T2-Statistic. 6. Classification of Observations. 7. The Distribution of the Sample Covariance Matrix and the Sample Generalized Variance. 8. Testing the General Linear Hypothesis: Multivariate Analysis of Variance 9. Testing Independence of Sets of Variates. 10. Testing Hypotheses of Equality of Covariance Matrices and Equality of Mean Vectors and Covariance Matrices. 11. Principal Components. 12. Cononical Correlations and Cononical Variables. 13. The Distributions of Characteristic Roots and Vectors. 14. Factor Analysis. 15. Pattern of Dependence; Graphical Models. Appendix A: Matrix Theory. Appendix B: Tables. References. Index. 25 cm..
9780471360919

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Modern experimental design

]]> By Ryan, Thomas P..
Hoboken, N.J. Wiley-Interscience 2007 .
xvi, 593 , INCLUDES INDEX 25 cm..
9780471210771

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Categorical data analysis /

]]> By Agresti, Alan..
Hoboken, NJ : Wiley, 2013 .
xvi, 714 p. : , TABLE OF CONTENTS Preface xiii 1 Introduction: Distributions and Inference for Categorical Data 1 1.1 Categorical Response Data, 1 1.2 Distributions for Categorical Data, 5 1.3 Statistical Inference for Categorical Data, 8 1.4 Statistical Inference for Binomial Parameters, 13 1.5 Statistical Inference for Multinomial Parameters, 17 1.6 Bayesian Inference for Binomial and Multinomial Parameters, 22 Notes, 27 Exercises, 28 2 Describing Contingency Tables 37 2.1 Probability Structure for Contingency Tables, 37 2.2 Comparing Two Proportions, 43 2.3 Conditional Association in Stratified 2 × 2 Tables, 47 2.4 Measuring Association in I × J Tables, 54 Notes, 60 Exercises, 60 3 Inference for Two-Way Contingency Tables 69 3.1 Confidence Intervals for Association Parameters, 69 3.2 Testing Independence in Two-way Contingency Tables, 75 3.3 Following-up Chi-Squared Tests, 80 3.4 Two-Way Tables with Ordered Classifications, 86 3.5 Small-Sample Inference for Contingency Tables, 90 3.6 Bayesian Inference for Two-way Contingency Tables, 96 3.7 Extensions for Multiway Tables and Nontabulated Responses, 100 Notes, 101 Exercises, 103 4 Introduction to Generalized Linear Models 113 4.1 The Generalized Linear Model, 113 4.2 Generalized Linear Models for Binary Data, 117 4.3 Generalized Linear Models for Counts and Rates, 122 4.4 Moments and Likelihood for Generalized Linear Models, 130 4.5 Inference and Model Checking for Generalized Linear Models, 136 4.6 Fitting Generalized Linear Models, 143 4.7 Quasi-Likelihood and Generalized Linear Models, 149 Notes, 152 Exercises, 153 5 Logistic Regression 163 5.1 Interpreting Parameters in Logistic Regression, 163 5.2 Inference for Logistic Regression, 169 5.3 Logistic Models with Categorical Predictors, 175 5.4 Multiple Logistic Regression, 182 5.5 Fitting Logistic Regression Models, 192 Notes, 195 Exercises, 196 6 Building, Checking, and Applying Logistic Regression Models 207 6.1 Strategies in Model Selection, 207 6.2 Logistic Regression Diagnostics, 215 6.3 Summarizing the Predictive Power of a Model, 221 6.4 Mantel–Haenszel and Related Methods for Multiple 2 × 2 Tables, 225 6.5 Detecting and Dealing with Infinite Estimates, 233 6.6 Sample Size and Power Considerations, 237 Notes, 241 Exercises, 243 7 Alternative Modeling of Binary Response Data 251 7.1 Probit and Complementary Log–log Models, 251 7.2 Bayesian Inference for Binary Regression, 257 7.3 Conditional Logistic Regression, 265 7.4 Smoothing: Kernels, Penalized Likelihood, Generalized Additive Models, 270 7.5 Issues in Analyzing High-Dimensional Categorical Data, 278 Notes, 285 Exercises, 287 8 Models for Multinomial Responses 293 8.1 Nominal Responses: Baseline-Category Logit Models, 293 8.2 Ordinal Responses: Cumulative Logit Models, 301 8.3 Ordinal Responses: Alternative Models, 308 8.4 Testing Conditional Independence in I × J × K Tables, 314 8.5 Discrete-Choice Models, 320 8.6 Bayesian Modeling of Multinomial Responses, 323 Notes, 326 Exercises, 329 9 Loglinear Models for Contingency Tables 339 9.1 Loglinear Models for Two-way Tables, 339 9.2 Loglinear Models for Independence and Interaction in Three-way Tables, 342 9.3 Inference for Loglinear Models, 348 9.4 Loglinear Models for Higher Dimensions, 350 9.5 Loglinear—Logistic Model Connection, 353 9.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions, 356 9.7 Loglinear Model Fitting: Iterative Methods and Their Application, 364 Notes, 368 Exercises, 369 10 Building and Extending Loglinear Models 377 10.1 Conditional Independence Graphs and Collapsibility, 377 10.2 Model Selection and Comparison, 380 10.3 Residuals for Detecting Cell-Specific Lack of Fit, 385 10.4 Modeling Ordinal Associations, 386 10.5 Generalized Loglinear and Association Models, Correlation Models, and Correspondence Analysis, 393 10.6 Empty Cells and Sparseness in Modeling Contingency Tables, 398 10.7 Bayesian Loglinear Modeling, 401 Notes, 404 Exercises, 407 11 Models for Matched Pairs 413 11.1 Comparing Dependent Proportions, 414 11.2 Conditional Logistic Regression for Binary Matched Pairs, 418 11.3 Marginal Models for Square Contingency Tables, 424 11.4 Symmetry, Quasi-Symmetry, and Quasi-Independence, 426 11.5 Measuring Agreement Between Observers, 432 11.6 Bradley–Terry Model for Paired Preferences, 436 11.7 Marginal Models and Quasi-Symmetry Models for Matched Sets, 439 Notes, 443 Exercises, 445 12 Clustered Categorical Data: Marginal and Transitional Models 455 12.1 Marginal Modeling: Maximum Likelihood Approach, 456 12.2 Marginal Modeling: Generalized Estimating Equations (GEEs) Approach, 462 12.3 Quasi-Likelihood and Its GEE Multivariate Extension: Details, 465 12.4 Transitional Models: Markov Chain and Time Series Models, 473 Notes, 478 Exercises, 479 13 Clustered Categorical Data: Random Effects Models 489 13.1 Random Effects Modeling of Clustered Categorical Data, 489 13.2 Binary Responses: Logistic-Normal Model, 494 13.3 Examples of Random Effects Models for Binary Data, 498 13.4 Random Effects Models for Multinomial Data, 511 13.5 Multilevel Modeling, 515 13.6 GLMM Fitting, Inference, and Prediction, 519 13.7 Bayesian Multivariate Categorical Modeling, 523 Notes, 525 Exercises, 527 14 Other Mixture Models for Discrete Data 535 14.1 Latent Class Models, 535 14.2 Nonparametric Random Effects Models, 542 14.3 Beta-Binomial Models, 548 14.4 Negative Binomial Regression, 552 14.5 Poisson Regression with Random Effects, 555 Notes, 557 Exercises, 558 15 Non-Model-Based Classification and Clustering 565 15.1 Classification: Linear Discriminant Analysis, 565 15.2 Classification: Tree-Structured Prediction, 570 15.3 Cluster Analysis for Categorical Data, 576 Notes, 581 Exercises, 582 16 Large- and Small-Sample Theory for Multinomial Models 587 16.1 Delta Method, 587 16.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities, 592 16.3 Asymptotic Distributions of Residuals and Goodness-of-fit Statistics, 594 16.4 Asymptotic Distributions for Logit/Loglinear Models, 599 16.5 Small-Sample Significance Tests for Contingency Tables, 601 16.6 Small-Sample Confidence Intervals for Categorical Data, 603 16.7 Alternative Estimation Theory for Parametric Models, 610 Notes, 615 Exercises, 616 17 Historical Tour of Categorical Data Analysis 623 17.1 Pearson–Yule Association Controversy, 623 17.2 R. A. Fisher’s Contributions, 625 17.3 Logistic Regression, 627 17.4 Multiway Contingency Tables and Loglinear Models, 629 17.5 Bayesian Methods for Categorical Data, 633 17.6 A Look Forward, and Backward, 634 Appendix A Statistical Software for Categorical Data Analysis 637 Appendix B Chi-Squared Distribution Values 641 References 643 Author Index 689 Example Index 701 Subject Index 705 Appendix C Software Details for Text Examples (text website) 27 cm..
9780470463635 (hardback)

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The EM algorithm and extensions /

]]> By McLachlan, Geoffrey J.,.
Hoboken, N.J. : Wiley-Interscience, 2008 .
xxvii, 359 p. : 25 cm..
9780471201700 (cloth) | 0471201707 (cloth)

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Generalized, linear, and mixed models

]]> By McCulloch, Charles E..
Hoboken, N.J. : Wiley, 2008 .
xxv, 384 p. : 25 cm..
9780470073711

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Time series analysis : forecasting and control.

]]> New Jersey John Wiley 2016 .
xxvi, 669 pages : , TABLE OF CONTENTS PREFACE TO THE FIFTH EDITION xix PREFACE TO THE FOURTH EDITION xxiii PREFACE TO THE THIRD EDITION xxv 1 Introduction 1 1.1 Five Important Practical Problems 2 1.2 Stochastic and Deterministic Dynamic Mathematical Models 6 1.3 Basic Ideas in Model Building 14 Appendix A1.1 Use of the R Software 17 Exercises 18 PART ONE STOCHASTIC MODELS AND THEIR FORECASTING 19 2 Autocorrelation Function and Spectrum of Stationary Processes 21 2.1 Autocorrelation Properties of Stationary Models 21 2.2 Spectral Properties of Stationary Models 34 Appendix A2.1 Link Between the Sample Spectrum and Autocovariance Function Estimate 43 Exercises 44 3 Linear Stationary Models 47 3.1 General Linear Process 47 3.2 Autoregressive Processes 54 3.3 Moving Average Processes 68 3.4 Mixed Autoregressive--Moving Average Processes 75 Appendix A3.1 Autocovariances Autocovariance Generating Function and Stationarity Conditions for a General Linear Process 82 Appendix A3.2 Recursive Method for Calculating Estimates of Autoregressive Parameters 84 Exercises 86 4 Linear Nonstationary Models 88 4.1 Autoregressive Integrated Moving Average Processes 88 4.2 Three Explicit Forms for the ARIMA Model 97 4.3 Integrated Moving Average Processes 106 Appendix A4.1 Linear Difference Equations 116 Appendix A4.2 IMA(0 1 1) Process with Deterministic Drift 121 Appendix A4.3 ARIMA Processes with Added Noise 122 Exercises 126 5 Forecasting 129 5.1 Minimum Mean Square Error Forecasts and Their Properties 129 5.2 Calculating Forecasts and Probability Limits 135 5.3 Forecast Function and Forecast Weights 139 5.4 Examples of Forecast Functions and Their Updating 144 5.5 Use of State-Space Model Formulation for Exact Forecasting 155 5.6 Summary 162 Appendix A5.1 Correlation Between Forecast Errors 164 Appendix A5.2 Forecast Weights for any Lead Time 166 Appendix A5.3 Forecasting in Terms of the General Integrated Form 168 Exercises 174 PART TWO STOCHASTIC MODEL BUILDING 177 6 Model Identification 179 6.1 Objectives of Identification 179 6.2 Identification Techniques 180 6.3 Initial Estimates for the Parameters 194 6.4 Model Multiplicity 202 Appendix A6.1 Expected Behavior of the Estimated Autocorrelation Function for a Nonstationary Process 206 Exercises 207 7 Parameter Estimation 209 7.1 Study of the Likelihood and Sum-of-Squares Functions 209 7.2 Nonlinear Estimation 226 7.3 Some Estimation Results for Specific Models 236 7.4 Likelihood Function Based on the State-Space Model 242 7.5 Estimation Using Bayes’ Theorem 245 Appendix A7.1 Review of Normal Distribution Theory 251 Appendix A7.2 Review of Linear Least-Squares Theory 256 Appendix A7.3 Exact Likelihood Function for Moving Average and Mixed Processes 259 Appendix A7.4 Exact Likelihood Function for an Autoregressive Process 266 Appendix A7.5 Asymptotic Distribution of Estimators for Autoregressive Models 274 Appendix A7.6 Examples of the Effect of Parameter Estimation Errors on Variances of Forecast Errors and Probability Limits for Forecasts 277 Appendix A7.7 Special Note on Estimation ofMoving Average Parameters 280 Exercises 280 8 Model Diagnostic Checking 284 8.1 Checking the Stochastic Model 284 8.2 Diagnostic Checks Applied to Residuals 287 8.3 Use of Residuals to Modify the Model 301 Exercises 303 9 Analysis of Seasonal Time Series 305 9.1 Parsimonious Models for Seasonal Time Series 305 9.2 Representation of the Airline Data by a Multiplicative (0 1 1) × (0 1 1)12 Model 310 9.3 Some Aspects of More General Seasonal ARIMA Models 325 9.4 Structural Component Models and Deterministic Seasonal Components 331 9.5 Regression Models with Time Series Error Terms 339 Appendix A9.1 Autocovariances for Some Seasonal Models 345 Exercises 349 10 Additional Topics and Extensions 352 10.1 Tests for Unit Roots in ARIMA Models 353 10.2 Conditional Heteroscedastic Models 361 10.3 Nonlinear Time Series Models 377 10.4 Long Memory Time Series Processes 385 Exercises 392 PART THREE TRANSFER FUNCTION AND MULTIVARIATE MODEL BUILDING 395 11 Transfer Function Models 397 11.1 Linear Transfer Function Models 397 11.2 Discrete Dynamic Models Represented by Difference Equations 404 11.3 Relation Between Discrete and Continuous Models 414 Appendix A11.1 Continuous Models with Pulsed Inputs 420 Appendix A11.2 Nonlinear Transfer Functions and Linearization 424 Exercises 426 12 Identification Fitting and Checking of Transfer Function Models 428 12.1 Cross-Correlation Function 429 12.2 Identification of Transfer Function Models 435 12.3 Fitting and Checking Transfer Function Models 446 12.4 Some Examples of Fitting and Checking Transfer Function Models 453 12.5 Forecasting with Transfer FunctionModels Using Leading Indicators 461 12.6 Some Aspects of the Design of Experiments to Estimate Transfer Functions 469 Appendix A12.1 Use of Cross-Spectral Analysis for Transfer Function Model Identification 471 Appendix A12.2 Choice of Input to Provide Optimal Parameter Estimates 473 Exercises 477 13 Intervention Analysis Outlier Detection and Missing Values 481 13.1 Intervention Analysis Methods 481 13.2 Outlier Analysis for Time Series 488 13.3 Estimation for ARMA Models with Missing Values 495 Exercises 502 14 Multivariate Time Series Analysis 505 14.1 Stationary Multivariate Time Series 506 14.2 Vector Autoregressive Models 509 14.3 Vector Moving Average Models 524 14.4 Vector Autoregressive--Moving Average Models 527 14.5 Forecasting for Vector Autoregressive--Moving Average Processes 534 14.6 State-Space Form of the VARMA Model 536 14.7 Further Discussion of VARMA Model Specification 539 14.8 Nonstationarity and Cointegration 546 Appendix A14.1 Spectral Characteristics and Linear Filtering Relations for Stationary Multivariate Processes 552 Exercises 554 PART FOUR DESIGN OF DISCRETE CONTROL SCHEMES 559 15 Aspects of Process Control 561 15.1 Process Monitoring and Process Adjustment 562 15.2 Process Adjustment Using Feedback Control 566 15.3 Excessive Adjustment Sometimes Required by MMSE Control 580 15.4 Minimum Cost Control with Fixed Costs of Adjustment and Monitoring 582 15.5 Feedforward Control 588 15.6 Monitoring Values of Parameters of Forecasting and Feedback Adjustment Schemes 599 Appendix A15.1 Feedback Control Schemes Where the Adjustment Variance Is Restricted 600 Appendix A15.2 Choice of the Sampling Interval 609 Exercises 613 PART FIVE CHARTS AND TABLES 617 COLLECTION OF TABLES AND CHARTS 619 COLLECTION OF TIME SERIES USED FOR EXAMPLES IN THE TEXT AND IN EXERCISES 625 REFERENCES 642 INDEX 659 26 cm.H.B..
9781118675021

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Statistical analysis with missing data

]]> By Little, Roderick J.A. .
USA Wiley 2020 .
xii, 449 , Includes index & reference 9780470526798

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