Statistical Modelling with Quantile Functions
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A01=Warren Gilchrist
advanced quantile modeling applications
Author_Warren Gilchrist
Average Run Length
bivariate distributions
Burr XII Distribution
Category=PBT
Category=PBWH
CDF
Conditional CDF
Cumulative Distribution Function
data analysis techniques
Density Probability Plot
distribution
distribution fitting
distributional
Distributional Range
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
exponential
Exponential Distribution
Exponentiated Weibull Distribution
Fitted Model
generalized
Generalized Lambda Distribution
Generalized Pareto
Generalized Pareto Distribution
lambda
logistic
Non-decreasing Function
parameter
Pareto Distribution
probability theory
QF
Quantile Density Function
Quantile Function
Quantile Model
Regression Quantile
Regression Quantile Function
Regression Quantile Models
scale
shape
statistical estimation methods
stochastic modeling
Unit Exponential Distribution
Vice Versa
weibull
Weibull Distribution
Product details
- ISBN 9781584881742
- Weight: 790g
- Dimensions: 156 x 234mm
- Publication Date: 15 May 2000
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Galton used quantiles more than a hundred years ago in describing data. Tukey and Parzen used them in the 60s and 70s in describing populations. Since then, the authors of many papers, both theoretical and practical, have used various aspects of quantiles in their work. Until now, however, no one put all the ideas together to form what turns out to be a general approach to statistics.
Statistical Modelling with Quantile Functions does just that. It systematically examines the entire process of statistical modelling, starting with using the quantile function to define continuous distributions. The author shows that by using this approach, it becomes possible to develop complex distributional models from simple components. A modelling kit can be developed that applies to the whole model - deterministic and stochastic components - and this kit operates by adding, multiplying, and transforming distributions rather than data.
Statistical Modelling with Quantile Functions adds a new dimension to the practice of statistical modelling that will be of value to anyone faced with analyzing data. Not intended to replace classical approaches but to supplement them, it will make some of the traditional topics easier and clearer, and help readers build and investigate models for their own practical statistical problems.
Gilchrist, Warren
