Difference between revisions of "Booklist: probability and statistics"

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(Created page with "# D. Huff, *How to Lie with Statistics*. # Mood, Graybill, and Boes, *Introduction to the Theory of Statistics*, 3rd ed., 1974. # Seber & Lee, *Linear Regression Analysis*,...")
 
 
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# D. Huff, *How to Lie with Statistics*.
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# D. Huff, ''How to Lie with Statistics''.
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# Mood, Graybill, and Boes, ''Introduction to the Theory of Statistics'', 3rd ed., 1974.
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# Seber & Lee, ''Linear Regression Analysis'', 2nd ed.
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# Hastie, Tibshirani, and Friedman, ''Elements of Statistical Learning'', 2nd ed., 2009.
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# A. Agresti, ''Categorical Data Analysis'', 2nd ed.
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# Boyd & Vandenberghe, ''Convex Optimization''.
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# Efron & Tibshirani, ''An Introduction to the Bootstrap''.
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# J. Liu, ''Monte Carlo Strategies in Scientific Computing'' or P. Glasserman, ''Monte Carlo Methods in Financial Engineering''.
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# E. Tufte, ''The Visual Display of Quantitative Information''.
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# J. Tukey, ''Exploratory Data Analysis''.
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# F. A. Graybill, ''Theory and Application of the Linear Model''.
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# F. A. Graybill, ''Matrices with Applications in Statistics''.
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# Devroye, Gyorfi, and Lugosi, ''A Probabilistic Theory of Pattern Recognition''.
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# Brockwell & Davis, ''Time Series: Theory and Methods''.
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# Motwani and Raghavan, ''Randomized Algorithms''.
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# D. Williams, ''Probability and Martingales''
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# R. Durrett, ''Probability: Theory and Examples''.
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# F. Harrell, ''Regression Modeling Strategies''.
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# Lehman and Casella, ''Theory of Point Estimation''.
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# Lehmann and Romano, ''Testing Statistical Hypotheses''.
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# A. van der Vaart, ''Asymptotic Statistics''.
  
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# Mood, Graybill, and Boes, *Introduction to the Theory of Statistics*, 3rd ed., 1974.
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== Metadata ==
  
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# Seber & Lee, *Linear Regression Analysis*, 2nd ed.
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* Original author: [[User:Datacraftsnet]]
  
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# Hastie, Tibshirani, and Friedman, *Elements of Statistical Learning*, 2nd ed., 2009.
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== External links ==
  
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# A. Agresti, *Categorical Data Analysis*, 2nd ed.  
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* A related [http://stats.stackexchange.com/questions/6538/mathematician-wants-the-equivalent-knowledge-to-a-quality-stats-degree stats.stackexchange.com thread].
  
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# Boyd & Vandenberghe, *Convex Optimization*.
 
  
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# Efron & Tibshirani, *An Introduction to the Bootstrap*.
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[[Category:BookList]]
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# J. Liu, *Monte Carlo Strategies in Scientific Computing* or P. Glasserman, *Monte Carlo Methods in Financial Engineering*.
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# E. Tufte, *The Visual Display of Quantitative Information*.
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# J. Tukey, *Exploratory Data Analysis*.
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# F. A. Graybill, *Theory and Application of the Linear Model*.
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# F. A. Graybill, *Matrices with Applications in Statistics*.
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# Devroye, Gyorfi, and Lugosi, *A Probabilistic Theory of Pattern Recognition*.
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# Brockwell & Davis, *Time Series: Theory and Methods*.
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# Motwani and Raghavan, *Randomized Algorithms*.
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# D. Williams, *Probability and Martingales* and/or R. Durrett, *Probability: Theory and Examples*.
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# F. Harrell, *Regression Modeling Strategies*.
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# Lehman and Casella, *Theory of Point Estimation*.
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# Lehmann and Romano, *Testing Statistical Hypotheses*.
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# A. van der Vaart, *Asymptotic Statistics*.
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Latest revision as of 09:02, 7 June 2014

  1. D. Huff, How to Lie with Statistics.
  2. Mood, Graybill, and Boes, Introduction to the Theory of Statistics, 3rd ed., 1974.
  3. Seber & Lee, Linear Regression Analysis, 2nd ed.
  4. Hastie, Tibshirani, and Friedman, Elements of Statistical Learning, 2nd ed., 2009.
  5. A. Agresti, Categorical Data Analysis, 2nd ed.
  6. Boyd & Vandenberghe, Convex Optimization.
  7. Efron & Tibshirani, An Introduction to the Bootstrap.
  8. J. Liu, Monte Carlo Strategies in Scientific Computing or P. Glasserman, Monte Carlo Methods in Financial Engineering.
  9. E. Tufte, The Visual Display of Quantitative Information.
  10. J. Tukey, Exploratory Data Analysis.
  11. F. A. Graybill, Theory and Application of the Linear Model.
  12. F. A. Graybill, Matrices with Applications in Statistics.
  13. Devroye, Gyorfi, and Lugosi, A Probabilistic Theory of Pattern Recognition.
  14. Brockwell & Davis, Time Series: Theory and Methods.
  15. Motwani and Raghavan, Randomized Algorithms.
  16. D. Williams, Probability and Martingales
  17. R. Durrett, Probability: Theory and Examples.
  18. F. Harrell, Regression Modeling Strategies.
  19. Lehman and Casella, Theory of Point Estimation.
  20. Lehmann and Romano, Testing Statistical Hypotheses.
  21. A. van der Vaart, Asymptotic Statistics.

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