Density Estimation and Combination under Model Ambiguity



where the last equality holds since k(u) = —k(—u)), we can notice that Wn(θ*) = λ∕-Un.

Applying first Lemma 3.1 and then Theorem 3.1 in Powell, Stock, and Stoker (1989), or similarly Lemma
3.3b. in Zheng (1996) we can show that
Wn(θ) is asymptotically normally distributed and that it is
Op (√1n) . Let define Hn in the following way:

Hn(xi,Xj ) = h K ( xj


x

-i)[s(θ,xi) — s(θ,xj∙)].


(42)


First, we need to verify that E ∣j∣Hn(xi,Xj)∣∣2j = o(n). Let define v2(θ,x) = E(s2(θ, x)/x) and v(θ*,x) =
E(s(θ, x)/x),


Hn(xi,xj)∣∣2∣ = E E ^∣Hn(xi,xj)2 /xi,xj∙)] =


(43)


1     °    xj

= h2   K( -j


— xi
h


°2

)   [v2(θ*,xi) + v2(θ,xj) 2v(θ,xi)v(θ*,xj)] g(xi)g(xj)dxidxj =


now using the change of variable from (xi, xj ) to (xi, u = —


—) we obtain


= — ʃ ∣∣K(u)∣∣2 £v2(θ*, xi) + v2(θ*, xi + hu) 2v(θ, xi)v(θ*, xi + hu)] g(xiι)g(xi + hu)dxihdu =


= O(—) = O(n(nh) 1 ) = o(n) since nh → ∞.
h


(44)


This implies that ʌ/-(Un — Un) = op(1). Thus, we need just to study the behavior of Un which is given by


2

Un = E(rn(xi)) + - 2^rn(xi) — Ε(rn(xi)).

i

Let compute rn (xi) which is defined in the following way:


(45)


rn(xi) — E (Hn(xi,xj )/xi) — hK (

x

— ) [s(θ ,xi) — v(θ ,xj)] g(xj)dxj =

(46)


where


and


= — K K(u)[s(θ,xi) — v(θ,xi + hu)] g(xi + hu)hdu = r(xi) + tn(xi)
h


r(xi) = [s(θ, xi) — v(θ, xi)] g(xi)


tn(xi) =       ,θ θ. u2K (u)du = h


(47)


(48)


(49)


This last expression has been obtained applying the mean value theorem to v(θ*, Xi + hu) and g(xi + hu),
whichyields v(θ*,Xi) + huv0(xi, θ*) and g(θ*,xi) + hug0(xi*) where xit lies in [xi,xi + hu].

Further, we need to compute E(rn (xi)) = E(Hn(xi, xj))


24




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