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outline |
note |
Chap 1 |
M- and Z-estimator |
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§01 |
Introduction |
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§02 |
Consistency |
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le01
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§03 |
Asymptotic normality |
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le02
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Chap 2 |
Asymptotic properties of tests |
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§04 |
Contiguity |
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le03
le04
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§05 |
Local asymptotic normality (LAN) |
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le05
le06
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§06 |
Asymptotic relative efficiency (ARE) |
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le07
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§07 |
Rank tests |
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§08 |
Asymptotic power of rank tests |
|
le08
le09a
|
Chap 3 |
Nonparametric estimation by projection |
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§09 |
Review |
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§10 |
Noisy version of the parameter |
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le09b
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§11 |
Orthogonal projection |
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le10
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§12 |
Orthogonal projection estimator |
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le11
le12
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§13 |
Minimax optimal estimation |
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le13
le14
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§14 |
Data-driven estimation |
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le15
le16
le17a
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Chap 4 |
Nonparametric density estimation |
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§15 |
Noisy density coefficients |
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le17b
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§16 |
Projection density estimator |
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le18
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§17 |
Minimax optimal density estimation |
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le19
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§18 |
Data-driven density estimation |
|
le20
le21
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Chap 5 |
Nonparametric regression |
§01-§22 |
(02/01/2023) |
§19 |
Noisy regression coefficients |
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le22
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§20 |
Projection regression estimator |
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le23
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§21 |
Minimax optimal regression/td>
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le24
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§18 |
Data-driven regression |
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le25
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