Springer Texts in Statistics
Label
Springer Texts in Statistics
Name
Springer Texts in Statistics
Actions
Incoming Resources
- Has instance2
- Has parts29
- Statistics and Data Analysis for Financial Engineering, with R examples, by David Ruppert, David S. Matteson
- Probability with Applications in Engineering, Science, and Technology, by Matthew A. Carlton, Jay L. Devore
- Statistical Methods for Quality Assurance, Basics, Measurement, Control, Capability, and Improvement, by Stephen B. Vardeman, J. Marcus Jobe
- Lectures on Categorical Data Analysis, by Tamás Rudas
- Intuitive Introductory Statistics, by Douglas A. Wolfe, Grant Schneider
- Bayesian Inference of State Space Models, Kalman Filtering and Beyond, by Kostas Triantafyllopoulos
- Bayes Factors for Forensic Decision Analyses with R, by Silvia Bozza, Franco Taroni, Alex Biedermann
- Design and Analysis of Experiments, by Angela Dean, Daniel Voss, Danel Draguljić
- Probability for statisticians, by Galen R. Shorack
- Probability with Applications in Engineering, Science, and Technology, by Matthew A. Carlton, Jay L. Devore
- Fundamentals of High-Dimensional Statistics, With Exercises and R Labs, by Johannes Lederer
- Basics of Modern Mathematical Statistics, by Vladimir Spokoiny, Thorsten Dickhaus
- Testing Statistical Hypotheses, by E.L. Lehmann, Joseph P. Romano
- Modern Mathematical Statistics with Applications, by Jay L. Devore, Kenneth N. Berk, Matthew A. Carlton
- Large Sample Techniques for Statistics, by Jiming Jiang
- Matrix Algebra, Theory, Computations and Applications in Statistics, by James E. Gentle
- A Course in Mathematical Statistics and Large Sample Theory, by Rabi Bhattacharya, Lizhen Lin, Victor Patrangenaru
- Statistical Data Analysis Using SAS, Intermediate Statistical Methods, by Mervyn G. Marasinghe, Kenneth J. Koehler
- A Graduate Course on Statistical Inference, by Bing Li, G. Jogesh Babu
- Statistical Learning from a Regression Perspective, by Richard A. Berk
- Statistical Analysis and Data Display, An Intermediate Course with Examples in R, by Richard M. Heiberger, Burt Holland
- Introduction to Time Series and Forecasting, by Peter J. Brockwell, Richard A. Davis
- Statistical Learning from a Regression Perspective, by Richard A. Berk
- Statistics for Health Data Science, An Organic Approach, by Ruth Etzioni, Micha Mandel, Roman Gulati
- Generalized Linear Models With Examples in R, by Peter K. Dunn, Gordon K. Smyth
- Plane Answers to Complex Questions, The Theory of Linear Models, by Ronald Christensen
- Advanced Linear Modeling, Statistical Learning and Dependent Data, by Ronald Christensen
- An Introduction to Statistical Learning, with Applications in R, by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
- Essentials of Stochastic Processes, by Richard Durrett