Home » Data Analysis and Approximate Models
Product Image

Data Analysis and Approximate Models

Patrick Laurie Davies
€210.80
Quantity:
In stock with our UK publisher. 14-28 days
Delivery/Collection within 10-20 working days
14 days return policy Shipping & Delivery
2`1 +1
A01=Patrick Laurie Davies
advanced statistical methodology
Affine Equivariance
Affinely Invariant
Approximation Intervals
Approximation Region
Asymptotic Quantiles
Asymptotics And Model Choice
Author_Patrick Laurie Davies
BIC Model
Breakdown Point
Category=PBT
chi-squared discrepancy
Copper Data
Critique Of Statistics
Data Set
Distribution Function
Empirical Distribution Function
Empirical Distribution Function Fn
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Equal Bin Widths
Finite Sample Breakdown Point
Finite Sample Version
Higher-Way Anova
Inter-laboratory Tests
Kullback Leibler Discrepancy
Kullback?Leibler And Chi-Squared Discrepancies
Location Functional
location-scale problem
Nonparametric Regression
Nonparametric Regression Based On Approximation
probability model approximation techniques
Probability Models As Approximations
Problems With Likelihood Principle
robust statistics
Simple GARCH
Simple GARCH Model
Smoothing Parameters In Image Analysis
Statistical Analysis And Inference Based On Approximate Models
statistical model assessment
Taut String
time series inference
Total Variation Distance
total variation metric
total variation metric for discrete data
Total Variation Penalty
Truth In Bayesian And Frequentist Statistics

Product details

  • ISBN 9781482215861
  • Weight: 540g
  • Dimensions: 156 x 234mm
  • Publication Date: 07 Jul 2014
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
Secure checkout Fast Shipping Easy returns
Description

The First Detailed Account of Statistical Analysis That Treats Models as Approximations

The idea of truth plays a role in both Bayesian and frequentist statistics. The Bayesian concept of coherence is based on the fact that two different models or parameter values cannot both be true. Frequentist statistics is formulated as the problem of estimating the "true but unknown" parameter value that generated the data.

Forgoing any concept of truth, Data Analysis and Approximate Models: Model Choice, Location-Scale, Analysis of Variance, Nonparametric Regression and Image Analysis presents statistical analysis/inference based on approximate models. Developed by the author, this approach consistently treats models as approximations to data, not to some underlying truth.

The author develops a concept of approximation for probability models with applications to:

  • Discrete data
  • Location scale
  • Analysis of variance (ANOVA)
  • Nonparametric regression, image analysis, and densities
  • Time series
  • Model choice

The book first highlights problems with concepts such as likelihood and efficiency and covers the definition of approximation and its consequences. A chapter on discrete data then presents the total variation metric as well as the Kullback–Leibler and chi-squared discrepancies as measures of fit. After focusing on outliers, the book discusses the location-scale problem, including approximation intervals, and gives a new treatment of higher-way ANOVA. The next several chapters describe novel procedures of nonparametric regression based on approximation. The final chapter assesses a range of statistical topics, from the likelihood principle to asymptotics and model choice.

More from this author