#
Springer Series in Statistics,
Resource Information
The series ** Springer Series in Statistics,** represents a set of related resources, especially of a specified kind, found in **University of Manitoba Libraries**.

The Resource
Springer Series in Statistics,
Resource Information

The series

**Springer Series in Statistics,**represents a set of related resources, especially of a specified kind, found in**University of Manitoba Libraries**.- Label
- Springer Series in Statistics,

## Context

Context of Springer Series in Statistics,#### Members

No resources found

No enriched resources found

- Springer Series in Statistics,, 199
- Springer Series in Statistics,, 200
- Springer Series in Statistics,, 272
- Springer Series in Statistics,, 297
- Springer Series in Statistics,, 298
- A Comparison of the Bayesian and Frequentist Approaches to Estimation
- A Course on Point Processes
- A Modern Theory of Factorial Design
- ARCH Models and Financial Applications
- ARMA Model Identification
- Advanced Statistics : Description of Populations
- An Introduction to Copulas
- An Introduction to Stochastic Processes and Their Applications
- An Introduction to the Theory of Point Processes
- Analysis of Neural Data
- Applied Compositional Data Analysis : With Worked Examples in R
- Aspects of Risk Theory
- Asymptotics in Statistics : Some Basic Concepts
- Bayesian Forecasting and Dynamic Models
- Bayesian Nonparametric Data Analysis
- Bayesian Reliability
- Bayesian and Frequentist Regression Methods
- Conditional Specification of Statistical Models
- Continuous-Time Markov Chains : An Applications-Oriented Approach
- Correlated Data Analysis: Modeling, Analytics, and Applications
- Design of Observational Studies
- Dynamic Data Analysis : Modeling Data with Differential Equations
- Dynamic Mixed Models for Familial Longitudinal Data
- Elements of Multivariate Time Series Analysis
- Elements of Nonlinear Time Series Analysis and Forecasting
- Exact Statistical Methods for Data Analysis
- Exponential Families of Stochastic Processes
- Finite Mixture and Markov Switching Models
- Fitting Linear Relationships : A History of the Calculus of Observations 1750â€“1900
- Forecasting with Exponential Smoothing : The State Space Approach
- Functional Data Analysis
- Functional Data Analysis
- Functional and Shape Data Analysis
- Indirect Sampling
- Inequalities: Theory of Majorization and Its Applications
- Inference in Hidden Markov Models
- Interpolation of Spatial Data : Some Theory for Kriging
- Introduction to Empirical Processes and Semiparametric Inference
- Introduction to Nonparametric Estimation
- Introduction to Variance Estimation
- Life Distributions : Structure of Nonparametric, Semiparametric, and Parametric Families
- Linear Models : Least Squares and Alternatives
- Linear Models : Least Squares and Alternatives
- Linear Models and Generalizations : Least Squares and Alternatives
- Linear and Generalized Linear Mixed Models and Their Applications
- Longitudinal Categorical Data Analysis
- Maximum Penalized Likelihood Estimation : Volume II: Regression
- Model-based Geostatistics
- Modeling Discrete Time-to-Event Data
- Models for Discrete Longitudinal Data
- Models for Uncertainty in Educational Testing
- Modern Multidimensional Scaling : Theory and Applications
- Modern Multidimensional Scaling : Theory and Applications
- Monte Carlo and Quasi-Monte Carlo Sampling
- Multiple Testing Procedures with Applications to Genomics
- Multiscale Modeling : A Bayesian Perspective
- Multivariate Analysis with LISREL
- Multivariate Statistical Modelling Based on Generalized Linear Models
- Nonlinear Estimation
- Nonparametric Curve Estimation : Methods, Theory, and Applications
- Nonparametric Functional Data Analysis : Theory and Practice
- Nonparametric Smoothing and Lack-of-Fit Tests
- Observational Studies
- Orthogonal Arrays : Theory and Applications
- Permutation Methods : A Distance Function Approach
- Permutation Tests : A Practical Guide to Resampling Methods for Testing Hypotheses
- Permutation, Parametric, and Bootstrap Tests of Hypotheses
- Prediction Theory for Finite Populations
- Principles and Theory for Data Mining and Machine Learning
- Prior Processes and Their Applications : Nonparametric Bayesian Estimation
- Quasi-Likelihood and its Application : A General Approach to Optimal Parameter Estimation
- Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs : Using R and SAS
- Recursive Partitioning and Applications
- Regression Modeling Strategies : With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis
- Reliability, Life Testing and the Prediction of Service Lives : For Engineers and Scientists
- Robust Asymptotic Statistics
- Sampling Algorithms
- Selected Papers of Frederick Mosteller
- Semiparametric Theory and Missing Data
- Semiparametric and Nonparametric Methods in Econometrics
- Shrinkage Estimation
- Simulation and Inference for Stochastic Differential Equations : With R Examples
- Smoothing Methods in Statistics
- Smoothing Techniques : With Implementation in S
- Spatial Statistics and Modeling
- Spectral Analysis of Large Dimensional Random Matrices
- Statistical Analysis of Environmental Space-Time Processes
- Statistical Analysis of Network Data : Methods and Models
- Statistical Analysis with Measurement Error or Misclassification : Strategy, Method and Application
- Statistical Decision Theory : Estimation, Testing, and Selection
- Statistical Demography and Forecasting
- Statistical Design and Analysis for Intercropping Experiments : Volume 1: Two Crops
- Statistical Design and Analysis for Intercropping Experiments : Volume II: Three or More Crops
- Statistical Learning from a Regression Perspective
- Statistical Methods in Software Engineering : Reliability and Risk
- Statistical Models Based on Counting Processes
- Statistical Tools for Nonlinear Regression : A Practical Guide with S-PLUS Examples
- Statistics for High-Dimensional Data : Methods, Theory and Applications
- Stochastic Orders
- Subsampling
- Targeted Learning : Causal Inference for Observational and Experimental Data
- Targeted Learning in Data Science : Causal Inference for Complex Longitudinal Studies
- Test Equating : Methods and Practices
- The Bootstrap and Edgeworth Expansion
- The Design and Analysis of Computer Experiments
- The Elements of Statistical Learning : Data Mining, Inference, and Prediction, Second Edition
- The Jackknife and Bootstrap
- The Linear Model and Hypothesis : A General Unifying Theory
- The Multivariate Normal Distribution
- The Statistical Theory of Shape
- Theory of Statistics
- Time Series: Theory and Methods
- Tools for Statistical Inference : Methods for the Exploration of Posterior Distributions and Likelihood Functions
- Vector Generalized Linear and Additive Models : With an Implementation in R
- Weak Convergence and Empirical Processes : With Applications to Statistics

## Embed

### Settings

Select options that apply then copy and paste the RDF/HTML data fragment to include in your application

Embed this data in a secure (HTTPS) page:

Layout options:

Include data citation:

<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.lib.umanitoba.ca/resource/PzLmqA2pq2E/" typeof="Series http://bibfra.me/vocab/lite/Series"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.lib.umanitoba.ca/resource/PzLmqA2pq2E/">Springer Series in Statistics,</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.lib.umanitoba.ca/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.lib.umanitoba.ca/">University of Manitoba Libraries</a></span></span></span></span></div>

Note: Adjust the width and height settings defined in the RDF/HTML code fragment to best match your requirements

### Preview

## Cite Data - Experimental

### Data Citation of the Series Springer Series in Statistics,

Copy and paste the following RDF/HTML data fragment to cite this resource

`<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.lib.umanitoba.ca/resource/PzLmqA2pq2E/" typeof="Series http://bibfra.me/vocab/lite/Series"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.lib.umanitoba.ca/resource/PzLmqA2pq2E/">Springer Series in Statistics,</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.lib.umanitoba.ca/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.lib.umanitoba.ca/">University of Manitoba Libraries</a></span></span></span></span></div>`