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Chap 1 
Preliminaries 



§01 
Fundamentals 


le01§01

§02 
Convergence of random variables 

le01

le01§02

§03 
Conditional expectation 


le02§03

Chap 2 
M and Zestimator 



§04 
Introduction / motivation / illustration 

le02
le03

le02§04.1
le03§04.2
le03§04.3

§05 
Consistency 

le04

le04§05.1
le04§05.2
le05§05.3

§06 
Asymptotic normality 

le05

le05§06.1
le06§06.2

Chap 3 
Asymptotic properties of tests 



§07 
Contiguity 

le06
le07
le08
le09

le06§07.1
le07§07.2
le07§07.3
le08§07.4
le08§07.5
le09§07.6
le09§07.7
le10§07.8

§08 
Local asymptotic normality (LAN) 

le10
le11
le12

le010§08.1
le011§08.2
le011§08.3
le012§08.4
le012§08.5

§09 
Asymptotic relative efficiency (ARE) 

le13 
le013§09

§10 
Rank tests 

le14

le013§10.1
le014§10.2
le014§10.3

§11 
Asymptotic power of rank tests 

le15

le015§11.1
le015§11.2

Chap 4 
Nonparametric estimation 
Sec. §01§17 (07/21/2020) 


§12 
Introduction 


le016§12

§13 
Kernel density estimation 

le16
le17
le18

le016§13.1
le017§13.2
le017§13.3
le018§13.4
le018§13.5

§14 
Nonparametric regression by local smoothing 

le19

le019§14.1
le019§14.2
le020§14.3

§15 
Sequence space model 

le20
le21
le22

le020§15.1
le021§15.2
le021§15.3
le022§15.4
le022§15.5

§16 
Orthogonal series estimation 

le23
le24/25

le023§16.1
le023§16.2
le024§16.3
le024§16.4

§17 
Supplementary materials 







