A01=David F. Andrews
A01=Frank R. Hampel
Absolute value
Accuracy and precision
Age Group_Uncategorized
Age Group_Uncategorized
Approximation
Author_David F. Andrews
Author_Frank R. Hampel
automatic-update
Bayes estimator
Bayesian
Big O notation
Calculation
Category1=Non-Fiction
Category=PBT
Combination
Confidence interval
Convergence of random variables
COP=United States
Cramer-Rao bound
Curvilinear coordinates
Degrees of freedom
Delivery_Pre-order
Effectiveness
Efficient estimator
eq_isMigrated=2
eq_nobargain
Estimation
Estimation theory
Estimator
Expected value
Extrapolation
For All Practical Purposes
Heavy-tailed distribution
Hypergeometric distribution
Indicator function
Interquartile range
Interval estimation
Invariant estimator
Language_English
Likelihood function
Likelihood-ratio test
Linear combination
Location parameter
Long tail
M-estimator
Main effect
Median absolute deviation
Mid-range
Normal distribution
Optimism
Order statistic
Orthogonal basis
Outlier
PA=Temporarily unavailable
Pairwise
Percentage point
Point estimation
Price_€100 and above
Probability
PS=Active
Quantile
Quantity
Quartile
Rate of convergence
Result
Robust regression
Robust statistics
Robustness
Scale invariance
Simple function
softlaunch
Special case
Spline interpolation
Standard deviation
Statistical significance
Stratified sampling
Summation
Trimmed Mean
Uniform distribution (continuous)
Uniform distribution (discrete)
Uniqueness
Variance
Weighted arithmetic mean
Winsorized mean
Winsorizing
Without loss of generality
Product details
- ISBN 9780691646633
- Weight: 879g
- Dimensions: 178 x 254mm
- Publication Date: 19 Apr 2016
- Publisher: Princeton University Press
- Publication City/Country: US
- Product Form: Hardback
- Language: English
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Because estimation involves inferring information about an unknown quantity on the basis of available data, the selection of an estimator is influenced by its ability to perform well under the conditions that are assumed to underlie the data. Since these conditions are never known exactly, the estimators chosen must be robust; i.e., they must be able to perform well under a variety of underlying conditions. The theory of robust estimation is based on specified properties of specified estimators under specified conditions. This book was written as the result of a study undertaken to establish the interaction of these three components over as large a range as possible. Originally published in 1972. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions.
The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
