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diagnostics of subsets of influential points
asymptotic representation
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Robust regression
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Robustness
3
consistency
3
Sensitivity analysis
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asymptotic normality
2
combining forecasts
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implicit weighting
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instrumental variables
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scale invariance
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the least weighted squares
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$\sqrt{n}$-consistency of estimate by instrumental weighted variables
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Classical and robust regression analysis
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Combination of forecasts
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Complexity of computation in robust estimation
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Consistency
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Consistency of estimate by instrumental weighted variables
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Diagnostics
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Implicit weighting
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Instrumental variables
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M -estimation
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M -estimators
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M-estimation
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Regression analysis
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Robust methods
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Robustification of classical instrumental variables
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The least weighted squares
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accuracy of prediction
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asymptotic distribution of the difference of M-estimators
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asymptotic normality of the least trimmed squares
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asymptotic representation of the estimate
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asymptotic representation of the instrumental weighted variables
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asymptotic representation of -estimators
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constrained M-estimators
1
critical values of robustified D- W statistics
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deviations of size and power of robustified likelihood ratio
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diversity of estimates
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diversity of estimators with high breakdown point
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efficiency for non-contaminated data
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Víšek, Jan
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Annals of the Institute of Statistical Mathematics
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Sensitivity Analysis of M-Estimates of Nonlinear Regression Model: Influence of Data Subsets
Víšek, Jan
- In:
Annals of the Institute of Statistical Mathematics
54
(
2002
)
2
,
pp. 261-290
Persistent link: https://www.econbiz.de/10005395562
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