Proceedings of the 2012 Winter Simulation Conference
C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A. M. Uhrmacher, eds.

EFFICIENT IMPORTANCE SAMPLING UNDER PARTIAL INFORMATIONHenry Lam
Boston University
111 Cummington Street
Boston, MA 02215, USA

ABSTRACT
Importance sampling is widely perceived as an indispensable tool in Monte Carlo estimation for rare-event
problems. It is also known, however, that constructing efficient importance sampling scheme requires inmany cases a precise knowledge of the underlying stochastic structure. This paper considers the simplest
problem in which part of the system is not directly known. Namely, we consider the tail probability ofa monotone function of sum of independent and identically distributed (i.i.d.) random variables, where
the function is only accessible through black-box simulation. A simple two-stage procedure is proposed
whereby the function is learned in the first stage before importance sampling is applied. We discuss somesufficient conditions for the procedure to retain asymptotic optimality in well-defined sense, and discuss
the optimal computational allocation. Simple analysis shows that the procedure is more beneficial than asingle-stage mixture-based importance sampler when the computational cost of learning is relatively light.
1

INTRODUCTION

Importance sampling has been well documented as one of the most useful tools in Monte Carlo estimation
related to rare events. In many settings, the importance sampler is constructed so as to mimic the zerovariance estimator, typically constructed by analyzing the sample path conditional on the occurrence of therare event of interest. Plenty of literature is devoted to developing the techniques in various systems; see,for example, Bucklew (2004), Juneja and Shahabuddin (2006), Heidelberger (1995), and Blanchet and Lam(2012) for general overview. The main analytical tools for such development include large deviations theory(some recent work for tackling general problems under this framework includes Dupuis and Wang 2007 andBlanchet and Glynn 2008), and adaptive approximation and h-transform (see, for example, L’Ecuyer andTuffin 2008 and Kollman et. al. 1999). Generally speaking, successful application of importance sampling
typically requires a sufficient level of knowledge on the problem structure. This also means, on the negativeside, that a poor judgement can substantially increase or even blow up the variance (Glasserman and Kou1995). Such a high sensitivity poses even greater emphasis on enough knowledge of the system structure.
As a classical example, consider the large deviations probability of sum of one-dimensional i.i.d. randomvariables. Under light-tail assumption, this large deviations is exponentially decaying, with the exponential
constant or so-called rate function dependent on the distribution of the summands and the cross-level ofthe probability. To construct an efficient importance sampler, these information for determining the ratefunction are needed in precision. If, for example, the target cross-level used by the importance sampler islower than the actual level, the variance of the resulting estimator can in fact grow exponentially in therarity parameter.
Our goal in this paper is to mathematically analyze the scenario when part of the system is not analyticallyknown, and as a result the parameter in the importance sampler cannot be accurately specified. We assume,
however, that knowledge on this missing part is accessible through data collection or simulation. The
goal as stated is of course very broad, so as a first attempt we shall confine our discussion on a concrete
problem regarding sum of i.i.d. random variables; namely, we consider the large deviations probability for

978-1-4673-4780-8/12/$31.00 ©2012 IEEELam
an unknown function of this i.i.d. sum. To be more precise, let us consider
P (µ(Sn ) > yn )(1)Pwhere Sn = ni=1 Ui is a sum of zero-mean i.i.d. random variables, µ(·) is a well-behaved function, andyn is a sequence of cross-level, with yn = cn for some constant c > 0. The quantity n acts as the “rarity”
parameter such that the probability (1) goes to zero as n → ∞.
We assume complete knowledge about Ui i.e., we know the analytical form of the distribution functionof Ui and also how to simulate Ui efficiently. The interesting feature about problem (1) lies in the functionµ(·), which is assumed to lack analytical closed-form expression, but nevertheless can be evaluated by
inputting arbitrary value to get, via a “black box”, the mapped function value subject to random noise. In
other words, we can simulate (or “collect data” from) a sequence of
Yt = µ(Xt ) + Zt (Xt )where Xt is the input at each time, and Zt (·) is random noise depending on the input. To get an example
for practical motivation, one can think about nested simulation in financial risk management. The Sn in(1) can be regarded as the random walk modeling an economic factor or stock movement, and we areinterested in estimating the tail risk of say an option portfolio. In this case µ(Sn ) is the sum of market
values of a set of options at underlying price Sn . If the options are non-standard and have no closed-form
expression, Monte Carlo method is necessary in estimating their values, and hence the value of µ(Sn ). In
this case µ(·) is not directly known in closed-form but can be simulated. For convenience, let us call thefunction µ(·) the mean function throughout this manuscript.
To further our discussion, we introduce some assumptions for this mean function µ(·). It is worth
pointing out that the model and assumptions considered in this paper are by no means practical in realapplications, but serve to facilitate a transparent illustration of our analysis. First, we assume that µ(·) issmooth enough and monotonic:
Assumption 1 The mean function µ(·) is differentiable with bounded positive derivative i.e., 0 < γ1 ≤µ0 (x) ≤ γ2 for all x ∈ R. The bounds γ1 and γ2 are known.
Let us denote Σ as the class of functions that satisfy Assumption 1, which implies in particular thatµ(·) is strictly increasing and grows steadily. This assumption, though restrictive, is natural for carrying
out nonparametric procedure. In fact, a shifted version of µ(·) can be shown to be quasi-linear, whichleads to tractable large deviations behavior (Woodroofe 1972). Moreover, note that when µ(·) is a known
function, the problem can be reduced to P (Sn > ξn ) where ξn is the root of µ(ξn ) = yn , which is thestandard large deviations problem for i.i.d. sum.
Our focus in this paper is on the amount of information we need to know about µ(·), quantified via thenumber of simulation trials or “data”, such that one can still retain efficiency of the importance sampler.
Learning the function takes up computation or other effort, so there is an intrinsic tradeoff on resource
allocation to learning versus carrying out the importance sampler. We shall propose and provide analysis
on this tradeoff for a simple two-stage procedure: learn about the function µ(·) in the first stage, and use
the gathered information to devise the importance sampler in the second stage. Throughout the paper, weshall use a minimax framework on the class Σ to address the above question.
Other than a simple two-stage procedure, another method is a uniformly efficient mixture-based
importance sampler, analyzed in Glasserman and Juneja (2008). The purpose of their algorithm was totackle the problem of simultaneous estimation for rare-event probabilities over a range of cross-levels,
also under a minimax framework. They consider a mixture of exponential tilting schemes with different
tilting parameters, and choose the best mixture probabilities and tilting parameters i.e., with minimax
risk. Although the initial motivation was different, their algorithm can be readily applied to our scenario
where the range of cross-levels is now inferred from the class Σ of mean functions. We will provide acomparison of this mixture algorithm with the simple two-stage approach we propose, and demonstrateLamunder what situation our algorithm works better. Intuitively, the two-stage procedure is preferable when thecomputational cost for learning is relatively light; in such case, the mixture algorithm, which is designed
to work well under the whole class Σ, sacrifices the efficiency that can otherwise be recovered by learning
more about µ(·) with little computational cost. Lastly, we should mention that adaptive approaches, suchas the cross-entropy method (Rubinstein and Kroese(2004)), can plausibly be applied to further improve
the performance; this will be a subject of further research.
We also note the paper by Zhang et. al. (2007), which points out the issue of complex system descriptionthat hinders the design of optimal variance reduction algorithms for rare-event problems. There they focus
on the first hitting probabilities of Markov processes that are analytically complex, and propose algorithms
that perform favorably relative to crude Monte Carlo yet are unlikely to be optimal. Olvera-Cravioto (2007)
studies the relation on estimation error between the truncation levels of heavy-tailed service times and thelarge deviations thresholds for single-server queues. Finally, we also mention the paper of L’Ecuyer et. al.
(2010) that studies robustness properties of various rare-event estimators regarding their higher moments.
We state our assumptions on the i.i.d. summands and noise
as follows:
PprocessnAssumptions on i.i.d. random variables. We assume Sn = i=1 Ui , where Ui , i = 1, . . . , n are i.i.d.
zero-mean random variables on R with light tail i.e., ψ(θ) := log EeθUi < ∞ for θ in a neighborhood of
0. Let Dom(ψ) = {θ : ψ(θ) < ∞} be the domain of ψ. We assume that
Assumption 2 (Light Tail) The logarithmic moment generating function ψ(·) is twice continuously differentiable and is steep on its positive domain i.e., ψ 0 (θ) → ∞ as θ goes to the positive boundary of Dom(ψ).
R∞
Assumption 3 (Smoothness) We have −∞ |φU (θ)|dθ < ∞ where φU (·) = EeiθUj is the characteristic
function of Uj . Hence Uj has density on the real axis.
Assumptions on noise process. The noises Zt (·) are assumed to satisfy:
Assumption 4 The noise process Zt (·) is a sequence of i.i.d. random functions. Denote F (z; x) as the
distribution function of Zt (x) given x, withZ
ψ(θ; x) := eθz F (dz; x) ≤ c1
for some constant c1 > 0, for −2h1 ≤ θ ≤ 2h1 for some h1 > 0.
In other words, the moment generating function of the noises is uniformly bounded over x ∈ R for θ
close to 0. The constants c1 and h1 are not necessarily known. However, knowledge about the bounds γ1and γ2 in Assumption 1 is required.
The rest of the paper is as follows. In Section 2 we describe our two-stage procedure, followed by our
main result in Section 3. Section 4 is devoted to a brief discussion. All proofs are left to the appendix.
2

TWO-STAGE PROCEDURE AND EFFICIENCY CRITERIONAs mentioned before, since µ(·) is strictly increasing, the probability P (µ(Sn ) > yn ) can be rewritten
as P (Sn > ξn ) where ξn is the solution to µ(ξn ) = yn . Since the mean function µ(·) is unknown, one
would have to estimate the root ξn . Consequently, we use a natural two-stage approach. In Stage 1,(t)
we convert the problem into the form P (Sn > ξˆn ) by solving the equation µ(ξn ) = yn using standard(t)
Robbins-Monro procedure. Here ξˆn is an estimate of ξn using t iterations (we sometimes abbreviate as(t)ξˆn = ξˆn when no confusion arises). In Stage 2, we take the value ξˆn as the cross-level and carry out
standard state-independent exponential tilting according to ξˆn .
As the computational resources are not fully devoted to Monte Carlo simulation of the probability, wedefine logarithmic efficiency, or asymptotic optimality, by a criterion that involves the overall computational
allocation:

Lam
Definition 1 A sequence of scheme, parametrized by n, that is used to generate an estimator Zn for the
probability of interest is called asymptotically optimal if given any  > 0, a subexponential (in n) expectedamount of computational resources is enough to guarantee that the worst-case relative mean square error
M SE(Zn )<2µ∈Σ P (µ(Sn ) > yn )
sup

(2)

where M SE(Zn ) denotes the mean square error E(Zn − P (µ(Sn ) > yn ))2 .
By Markov inequality, criterion (2) also ensures that the ratio of Zn −P (µ(Sn ) > yn ) to P (µ(Sn ) > yn )deviates from a given constant with controlled probability.
To facilitate our discussion, we assume that each iteration of the Robbins-Monro procedure in Stage 1
consumes γ(n) computational resource, and each sample of the importance sampler in Stage 2 consumes
λ(n) computational resource. The quantity γ(n) and λ(n) are taken to be subexponential in n. In fact,
regarding each arithmetic operation and generation of random variable as one unit of computation, thentypically γ(n) = O(1) and λ(n) = O(n). Note that when µ(·) is known,
Stage 1 is not necessary and the√number of simulation trials in Stage 2 needed to sustain (2) is O( n) (by the exact√ asymptotic for i.i.d.
sum; see Bucklew 2004), which gives an expected computational cost of O(λ(n) n).
To get some intuition about M SE(Zn ), we write
M SE(Zn ) = V ar(Zn ) + (EZn − P (µ(Sn ) > yn ))2= E[V ar(Zn |ξˆn )] + V ar(E[Zn |ξˆn ]) + (E[E[Zn |ξˆn ]] − P (Sn > ξn ))2= E[V ar(Zn |ξˆn )] + M SE(E[Zn |ξˆn ])= E[V ar(Zn |ξˆn )] + M SE(P (Sn > ξˆn |ξˆn )).

(3)

The expression (3) can be interpreted as the decomposition of mean square error into Stages 1 and 2.
The first term captures the contribution to the mean square error due to estimation by importance samplingat Stage 2, taken as its variance averaged over the value of ξˆn obtained at Stage 1. The second term of(3) is the mean square error contributed by Stage 1, which can be interpreted as the expected squared bias
due to Stage 1.
To close this section, we list our assumptions on the Robbins-Monro procedure in Stage 1 for estimating
the solution of µ(ξn ) = yn , where yn = cn. Recall that we can observe Yt = µ(Xt ) + Zt (Xt ). We use
the updating rule Xt+1 = Xt − at [Yt − yn ]. Also, note that the assumption γ1 ≤ µ0 (·) ≤ γ2 implies that
m1 n ≤ ξn ≤ m2 n for m1 = c/γ2 and m2 = c/γ1 . Therefore, we truncate the estimate of ξˆn at the endof the Robbins-Monro procedure to the closest point in [m1 n, m2 n]. This truncation procedure can only
improve our estimate of ξˆn from a risk-theoretic perspective.
We assume the step size at and the initial value X0 satisfy the following assumptions:
Assumption 5 at = a/t where the constant a satisfies aγ1 ≥ 1, and γ1 is the constant in Assumption 1.
Assumption 6 EeθX0 < C(n) for θ in a neighborhood of 0, and C(n) grows at most exponentially in n.
Assumption 6 is natural. For example, X0 can be taken to be merely 0, or c0 n, where c0 is a constanthoping to match up the growth of yn . The choice of X0 indeed does not affect qualitatively the large
deviations behavior of ξˆn , and hence the overall mean square error, as shown by Theorem 2 in the sequel.
3

MAIN RESULTS

In this section we report and discuss a performance bound on our two-stage procedure.
3.1 Bound on Relative Mean Square Error
We denote t as the number of iterations in Stage 1 and m as the number of simulation using exponential
tilting in Stage 2. Our main result is the following:

Lam
Theorem 1 With Assumptions 1–6, and suppose the number of Stage 1 iterations t = ω(n) and t = o(ecn )for any c > 0 i.e., t is superlinear but subexponential. The relative mean square error satisfies√
M SE(Zn )
n n
=O
.
(4)
sup
+
2
m
t
µ∈Σ P (µ(Sn ) > yn )√This in particular shows that the two-stage scheme, by choosing m to be ω( n) and o(ecn ) and tchosen as in the theorem, is asymptotically optimal according to Definition 1.
3.2 Optimal Allocation
Now suppose we are given a computational budget H (possibly dependent on n). The bound (4) gives a
way to check if an allocation scheme achieves asymptotic optimality as in Definition 1. It also allows one
to minimize the mean square error via suitable resource allocation. Recall that the expected computational
cost per Stage 1 iterationis γ(n) and that for each Stage 2 simulation is λ(n). We then consider the√minimization of C1 n/m + C2 n/t under the constraint tγ(n) +√mλ(n) ≤ H, where C1 and C2 are somepositive constants. Applying Lagrange multiplier α gives C1 n/m2 = αλ(n) and C2 n/t2 = αγ(n).
Upon solving, we get√√√
C1 Hn1/4
C2 H nppppm= pand t = p.
( C1 λ(n)n1/4 + C2 γ(n)n) λ(n)( C1 λ(n)n1/4 + C2 γ(n)n) γ(n)This gives the minimal relative mean square error
!
p
p
p
p
p
√ p
n γ(n)( C1 λ(n)n1/4 + C2 γ(n)n)
n1/4 λ(n)( C1 λ(n)n1/4 + C2 γ(n)n)
+
O
H
H
!
p
p
( λ(n)n1/4 + γ(n)n)2
= O
.
(5)
H
In the scenario where γ(n) = 1 and λ(n) = n, we have m = O(H/n) and t = O(H/n1/4 ), which gives
a minimal relative mean square error of O(n3/2 /H).
3.3 Comparison to Mixture-Based Algorithm
As discussed, the problem P (µ(Sn ) > yn ) can be reformulated as P (Sn > ξn ) with ξn taking some valueon [m1 n, m2 n]. Consequently, another approach is to ignore learning the mean function µ(·) and carryout an importance sampling scheme that is uniformly asymptotically efficient on the interval [m1 n, m2 n].
Glasserman and Juneja (2008) investigates the efficiency of a mixture scheme that randomizes the target
cross-level between m1 and m2 , followed by the optimal exponential tilting with the realized target level.
Suppose that the mixture is discrete on k points on [m1 , m2 ], call them qi , i = 1, . . . , k. Moreover, let thediscrete probability be 1/k, which is shown to be nearly optimal in well-defined sense (Glasserman andJuneja 2008, Remark 3.1). We choose to avoid discussion on continuous mixture distribution as it requires
numerical integration and complicates the comparison.
The likelihood ratio can then be written as
1(6)
L = Pkθi Sn −nψ(θi )(1/k)ei=1where each θi satisfies ψ 0 (θi ) = qi . The resulting relative second moment per simulation run is√Ẽ[L2 ; µ(Sn ) > yn ]2
= O( nkenc/k )2
P (µ(Sn ) > yn )

(7)

Lam
for some constant c. In fact, from Glasserman and Juneja (2008) Proposition 3.3 (it appears that theconstant c in Equation (16) in their paper can be refined in terms of k) and further analysis (Lam 2012), theexpression on the right hand side of (7) can be shown to be also a minimax lower bound (with a different
constant in the exponent). Hence (7)√is a good benchmark for comparison with our two-stage procedure.
It is easy
√to see that picking k = Θ( n) would minimize (7) to attain an order n. So let us insist on usingk = Θ( n) in our comparison.
Note that the calculation of likelihood ratio (6) would add an order k arithmetic operations to the cost
per simulation, and this is taken as insignificant (to be conservative in √our comparison and also because
typically λ(n) = n). Given a budget capacity H, and choosing k = Θ( n), the expected relative second
moment for the mixture algorithm is of order nλ(n)/H. Comparing this to (5), we see that it is more
beneficial to use our two-stage procedure ifpp( C1 λ(n)n1/4 + C2 γ(n)n)2 ≤ nλ(n)which is implied by γ(n)/λ(n) = o(1). Therefore, it is better to pursue two-stage procedure if thecomputational cost for stochastic root finding is small relative to the cost of simulation, namely when
γ(n) = o(λ(n)).
4

DISCUSSION: UNCERTAIN INPUT PARAMETERS

Although this paper deals with the scenario involving an unknown function in the probability, similar
methodology can be utilized to analyze efficient importance sampling strategy when certain parameters inthe stochastic object are uncertain and has to be estimated through other data. For example, consider again
the large deviations probability of i.i.d. sum, where the distribution of the summands are known up to ashift of the mean i.e., the summands are Ui + µ P
for known distribution Ui butPµ is uncertain. If µ lies in aninterval [m1 n, m2 n], then the probability is P ( ni=1 Ui > yn − µn) = P ( ni=1 Ui > ξn ) where now ξnis again some value in a bounded interval [m01 n, m02 n]. The problem then quickly falls into the framework
discussed in this paper. Further work along this line includes other types of estimation for the Ui and theestablishment of information-theoretic lower bounds to identify the optimal procedures in these contexts.
5

PROOFS

5.1 Main Proof
In this section we provide the main arguments for proving Theorem 1. The following uniform large
deviations estimate of Robbins-Monro procedure is needed:
Theorem 2 Suppose Assumptions 1, 4, 5 and 6 are in place. Then Xt satisfies the following large deviations
result 1 2

exp − 2dtx
for 0 ≤ x ≤ dh
c5 n
P (|Xt+1 − ξn | ≥ x) ≤ 2(t + c4 e )1exp − 2 thx
for x ≥ dh
uniformly over n, for some constants d, h, c4 , c5 .
The proof uses heavily a result in Woodroofe (1972), and is left to the next subsection.
Next, we also have the following bound on the relative bias of P (Sn > ξˆn ) for ξˆn close to ξn , uniformly
over ξn ∈ [m1 n, m2 n]:Theorem 3 Suppose Assumptions 1–6 hold. For xn ≤ n for some small  > 0, we have
|P (Sn > ξˆn ) − P (Sn > ξn )|≤ C1 eC2 xn
P
(S
>
ξ
)
n
n
ξn ∈[m1 n,m2 n] |ξ̂n −ξn |<xn
sup

sup

(8)

Lam
for some constants C1 , C2 > 0. Moreover, when xn ≤  for some small  > 0, we have
|P (Sn > ξˆn ) − P (Sn > ξn )|≤ C3 x n
P (Sn > ξn )
ξn ∈[m1 n,m2 n] |ξ̂n −ξn |<xn
sup

sup

(9)

for some constant C3 > 0.
An analogous result holds for the expected variance in the first term of (3):Theorem 4 Suppose Assumptions 1–6 hold, and Zn is generated from one simulation sample. For xn ≤ nfor some small , we have√E[Zn2 |ξˆn ]≤ C1 eC2 xn n2
ξn ∈[m1 n,m2 n] |ξ̂n −ξn |<xn P (Sn > ξn )
sup

sup

(10)

for some constants C1 , C2 > 0. Moreover, for xn ≤  for some small , we have√E[Z 2 |ξˆn ]≤ C3 n2
ξn ∈[m1 n,m2 n] |ξ̂n −ξn |<xn P (Sn > ξn )
sup

sup

(11)

for some constant C3 > 0.
The proof of Theorem 3 is left to the end of this section, while the proof of Theorem 4 is similar andis skipped. With these estimates, we are ready to prove Theorem 1:
Proof of Theorem 1.
Let us analyze the two terms in (3) one by one, starting with the second term. Notethat since ξn , ξˆn ∈ [m1 n, m2 n], |P (Sn > ξˆn ) − P (Sn > ξn )| is bounded by O(ecn ) for some constant c
uniformly over ξn , ξˆn ∈ [m1 n, m2 n]. Therefore,
M SE(P (Sn > ξˆn )) = E(P (Sn > ξˆn ) − P (Sn > ξn ))2= E[(P (Sn > ξˆn ) − P (Sn > ξn ))2 ; |ξˆn − ξn | < xn ] + P (|ξˆn − ξn | ≥ xn )Cecn≤|P (Sn > ξˆn ) − P (Sn > ξn )|2 + P (|ξˆn − ξn | ≥ xn )Cecn

sup

(12)

|ξ̂n −ξn |<xn

where C is a constant. By Theorems 2 and 3, we have, from (12), that
M SE(P (Sn > ξˆn ))0≤ C10 eC2 xn + 2(t + c4 ec5 n )e−c6 txn Cecn
2
P
(S
>
ξ
)
n
n
ξn ∈[m1 n,m2 n]
supfor some constants C10 , C20 , C, c, c6 > 0 when xn = Ω(1), which can be seen to be a suboptimal bound.
On the other hand, in the case that xn = o(1), we have
M SE(P (Sn > ξˆn ))2≤ C30 x2n + 2(t + c4 ec5 n )e−c7 txn Cecn
2
P
(S
>
ξ
)
n
n
ξn ∈[m1 n,m2 n]
sup

(13)

for some constant C30 , c7 > 0. For a given t, to minimize the right hand side of (13), we set−2 log xn = c7 tx2n − (c5 + c)npwhich gives xn = η n/t for large enough η > 0. Then the right hand side of (13) is of order O(n/t).
Next we analyze the first term in (3). Suppose first that Zn is generated from one Stage 2 simulation
sample. For an estimated value of ξn , namely ξˆn , we use exponential tilting with parameter θ̂n , the solutionLamto ψ 0 (θ̂n ) = ξˆn /n. Since the estimation procedure ensures that ξˆn is bounded in [m1 n, m2 n], the varianceof Zn , given any ξˆn , is uniformly bounded by Cecn for some constants C, c > 0. HenceE[V ar(Zn |ξˆn )] ≤ E[V ar(Z|ξˆn ); |ξˆn − ξn | < xn ] + P (|ξn − ξˆn | ≥ xn )Cecn= E[E[Z 2 |ξˆn ] − (E[Z|ξˆn ])2 ; |ξˆn − ξn | < xn ] + P (|ξn − ξˆn | ≥ xn )Cecn≤ E[E[Z 2 |ξˆn ]; |ξˆn − ξn | < xn ] + P (|ξn − ξˆn | ≥ xn )Cecn≤
sup E[Z 2 |ξˆn ] + P (|ξn − ξˆn | ≥ xn )Cecn .

(14)
(15)

|ξ̂n −ξn |<xn

From Theorem 2 and 4, for xn ≤ n,√
E[V ar(Zn |ξˆn )]0≤ C10 eC2 xn n + 2(t + c4 ec5 n )e−c6 txn Cecn2ξn ∈[m1 n,m2 n] P (Sn > ξn )supfor some constants C10 , C20 , C, c, c6 > 0. On the other hand, for xn ≤ , we haveE[V ar(Zn |ξˆn )]20√≤C
n + (t + c4 ec5 n )e−c7 txn Cecn
3
2
P
(S
>
ξ
)
n
n
ξn ∈[m1 n,m2 n]
supfor some constant C30 , c7 > 0. Recall the assumption that t = ω(n). Hence, to minimize the above
expressions, we choose xn → 0 arbitrarily slow to get√
E[V ar(Zn |ξˆn )]≤ C0 n2
ξn ∈[m1 n,m2 n] P (Sn > ξn )
sup

for√
some constant C 0 > 0. Lastly, for a sample mean of m trials of Zn , the relative expected variance isO( n/m). This concludes the theorem.
5.2 Proof of Theorem 2
We need the following result from Woodroofe (1972):
Theorem 5 (adapted from Woodroofe 1972) Consider a sequence of Xt , with Yt = µ(Xt ) + Zt (Xt ) as thevalue of the mean function µ(·) evaluated at Xt , corrupted with independent noise Zt (Xt ). The sequence
Xt is generated by the Robbins-Monro process Xt+1 = Xt − at Yt . We assume the following:
1’

2’
3’
4’

Zt (·) is a sequence of i.i.d.R random functions. Denote F (z; x) as the distribution function of Zt (x)given x, with ψ(θ; x) := eθz F (dz; x) ≤ c1R for some constant c1 > 0, and for −2h1 ≤ θ ≤ 2h1
for some h1 > 0. This in particular implies z 2 F (dz; x) ≤ c3 for some constant c3 > 0.
µ(0) = 0, and γ1 ≤ µ(x)/x ≤ γ2 for any x ∈ R \ {0}, for some γ1 , γ2 > 0 i.e., µ(·) is quasi-linear.
at = a/t, where the constant a satisfies aγ1 ≥ 1, with γ1 as defined in Assumption 2’ above.
EeθXr ≤ c2 for −h2 ≤ θ ≤ h2 for some h2 , c2 > 0, and for some r ≥ 5aγ2 /2.

Then we have the following inequalityP (|Xt+1 | ≥ x) ≤ 2(t + c2 )

 1 2
exp − 2dtxexp − 12 thx

for 0 ≤ x ≤ dh.
for x ≥ dh

Here d is a constant depending on a, γ1 , h1 , c1 , c3 , and h depends on h1 , h2 , a and r.
A few remarks are in place:

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Lam
Remark 1 In fact, from Woodroofe (1972), the constants d and h can be explicitly written as follows:P
Q
1. Let b = c3 +4c1 /h21 . Then d can be taken as bτ , where τ is an upper bound for nj=k na2j nm=j+1 (1−γ1 am ) < ∞.
2. h can be taken as min(h1 /(2a), h2 /r).
Remark 2 The formulation in Woodroofe (1972) focuses on the one-sided probability P (Xt+1 ≤ −x)(with the corresponding one-sided assumptions). The upper-tail result can be easily extended by taking
negative on both sides of the relation Xt+1 = Xt − at Yt to get −Xt+1 = −Xt − at (−Yt ), with −Yt now
becoming −µ(−(−Xt )) − Zt (−(−Xt )) with mean function −µ(−·) and noise −Zt (−·).
Proof of Theorem 2.
Note that µ(x) = yn can be rewritten as µ(x) − yn = 0, and we use the recursion
Xt+1 = Xt − at [Yt − yn ]. Consider the recursive process shifted by ξn i.e., let Wt = Xt − ξn , and write
Wt+1 = Wt − at [µ(Wt + ξn ) − yn ]. We will show that the recursion Wt satisfies Assumptions 1’, 2’, 3’
and 4’. Assumption 1’ is obvious. So we focus on the remaining ones.
Note that by Assumption 1 the expression µ(w + ξn ) − yn satisfies the inequality 0 < γ1 ≤ µ(w +ξn ) − yn ≤ γ2 by a straightforward invocation of the mean value theorem, where γ1 and γ2 are the constants
defined in Assumption 1. Moreover, obviously µ(0 + ξn ) − yn = 0. Hence Assumption 2’ is satisfied, andAssumption 3’ follows.
It remains to show Assumption 4’. Observe that EeθW0 = Eeθ(X0 −ξn ) ≤ C(n)Ee−θξn . By Assumption
1 µ(x) satisfies γ10 ≤ µ(x)/x ≤ γ20 for some γ10 , γ20 , and hence ξn = Θ(n). This implies Ee−θξn ≤ ecn forsome constant c. In overall, we then have EeθW0 ≤ c5 ec6 n for some constants c5 and c6 .
For convenience, write µ̃(w) = µ(w + ξn ) and Z̃t (w) = Zt (w + ξn ). Following the argument inWoodroofe (1972) p.338, we write
EeWt = Eeθ(Wt−1 −at µ̃(Wt−1 )−at Z̃t−1 (Wt−1 )) ≤ c2 Eeθ(Wt−1 −at µ̃(Wt−1 )) ≤ c2 (EeθWt−1 + Ee−at γ2 θWt−1 )Pfor θ in a small enough neighborhood of 0. Recursing the relation above leads to EeθWr ≤ c(r, c3 ) i Eeθi W0
where C(r, c3 ) is a constant depending on r and c3 and the summation is finite with each θi satisfying|θi | ≤ C(r, a, γ2 , θ). Therefore EeθWr ≤ c4 ec5 n for some constants c4 , c5 , and Assumption 4’ is satisfied.
The conclusion then follows directly. Finally, note that the inequality is also satisfied for an estimator thatprojects the last iteration onto the interval [m1 n, m2 n].
5.3 Proof of Theorem 3
Proof of Theorem 3.
Consider first the asymptotic for P (Sn > ξn ), where ξn ∈ [m1 n, m2 n]. Let θn be
0
the solution to ψ (θn ) = ξn /n, and let Ẽn [·] be the expectation under the exponential change of measurefor each Ui with parameter θn . We have
P (Sn > ξn ) = Ẽn [e−θn Sn +nψ(θn ) ; Sn > ξn ] = e−nI(ξn /n) Ẽn [e−θn (Sn −ξn ) ; Sn > ξn ]√ 00
= e−nI(ξn /n) Ẽn [e−θn nψ (θn )Zn ; Zn > 0](17)pwhere Zn = (Sn − ξn )/ nψ 00 (θn ) is a sum of i.i.d. variables with mean zero and unit variance underẼn [·]. Denoting Fn (·) as the distribution function of Zn , (17) can be written as
!
Z ∞
Z ∞
√
z
−nI(ξn /n)−nI(ξn /n)−z−θn nψ 00 (θn )zee
dFn (z) = e
e dFn pnψ 00 (θn )θn00
by a change of variable. Then integration by parts gives
!
#
Z ∞"
z
e−nI(ξn /n)
Fn p− Fn (0) e−z dz.
nψ 00 (θn )θn
0

(18)

Lam
We now consider sup|ξ̂n −ξn |<xn |P (Sn > ξˆn ) − P (Sn > ξn )| ≤ P (Sn > ξn − xn ) − P (Sn > ξn + xn ).
Suppose xn ≤ n for some small . By the same argument leading to (18), we have
 


Z ∞
z
Fn  q − Fn (0) e−z dz.
P (Sn > ξn ± xn ) = e−nI(ξn /n±xn /n)(19)0nψ 00 (θn± )θn±
Here θn± is the solution to the equations ψ 0 (θn± ) = ξn /n ± xn /n. Since ψ(·) is twice continuously
differentiable, ψ 0 (·) is continuously differentiable, and so is (ψ 0 )−1 (·) (defined as the positive root).
Moreover, by continuity ((ψ 0 )−1 )0 (s) is bounded over any bounded set of s. So
 


xn
xn
0 −1 ξn±0 −1 ξn= (ψ )±±((ψ 0 )−1 )0 (ζ)θn = (ψ )nnnn
for some ζ between ξn /n and ξn /n ± xn /n. Since ξn /n and ξn /n ± xn /n are uniformly bounded overn, ξn , we havexnθn± = θn ±
O(1)
(20)
n
uniformly in n, ξn . In particular, we have
ψ 00 (θn± ) ∼ ψ 00 (θn )(21)by continuity of ψ 00 (·).
Now







 ξ

ξn xn
xn
xn
xn
xn
n
± ξn
±
I
= θn
− ψ(θn ) = θn ±− ψ θn ±
±
±
O(1)
±
O(1)
n
n
n
n
n
n
n
n




xn xn
ξn x n
xn
ξn
±
O(1)
±
∓
O(1)ψ(ζ)(22)= θn − ψ(θn ) ± θn
n
n
n
n
n
n
for some ζ between θn and θn ± (xn /n)O(1). Since θn is uniformly bounded, by the uniform bound on
ξn /n and the continuity of (ψ 0 )−1 (·), (22) is written as
I(θn ) ±

xn
O(1)
n

uniformly over n, ξn . Then from (20), (21) and (23), the probability in (19) can be written as
!
#
(Z "
∞
z
e−nI(ξn /n)±xn O(1)Fn p
− Fn (0) e−z dznψ 00 (θn )θn
0
!
)
Z
xn ∞
z
z d
1
√
p
±
fn p
e−z dzn 0
n dx ψ 00 (θ(x))θ(x)nψ 00 (θn )θn

(23)

(24)

for some x in a compact interval, uniformly over n, ξn . Here θ(x)R ∞ is the function denoting the solution toψ 0 (θ) = x, and fn (·) denotes the density function of Zn . Since −∞ |φU (θ)|dθ < ∞, Edgeworth expansion√
gives |fn (x) − φ(x)| ≤ C/ n uniformly over x ∈ R, where φ(·) denotes the density function of standard
normal variable. This implies that fn (x) is uniformly bounded by some number M as n → ∞. Therefore,(24) can be rewritten as
"Z "
!
#
#
∞
z
x
n
e−nI(ξn /n)±xn O(1)Fn p
− Fn (0) e−z dz ± 3/2 O(1)nnψ 00 (θn )θn0

Lam
uniformly over n, ξn .
As a result, for xn ≤ n for small enough , we have
P (Sn > ξn − xn ) − P (Sn > ξn + xn ) ≤ e−nI(ξn /n)+xn O(1)

Z ∞"
Fn
0

!

z
p

nψ 00 (θn )θn#− Fn (0) e−z dz(25)whereas for xn ≤  for small enough , we have
P (Sn > ξn − xn ) − P (Sn > ξn + xn ) ≤ xn O(1)e−nI(ξn /n)

Z ∞"
Fn
0

z

!

p
nψ 00 (θn )θn#− Fn (0) e−z dz.
(26)

Let us now analyze the expression
Z ∞"
Fn

z

!

p

nψ 00 (θn )θn0#− Fn (0) e−z dz.

(27)

Following Dembo and Zeitouni (1998), Berry-Essen Theorem (or Edgeworth expansion up to the firstorder) states that(1 − z 2 )1√ φ(z) + o √Fn (z) = Φ(z) +
(28)
6 n
n
uniformly over z ∈ R, where Φ(z) and φ(z) are the distribution and density function of the standard normal
variable. Then (27) becomes
!
#
Z ∞"
z
Φ p
− Φ(0) e−z dz00nψ (θn )θn
0


!2 
!


Z ∞
1
z
1
φ p z√ 1 − p+
− φ(0) e−z dz + o √
(29)
6 n
n
nψ 00 (θn )θnnψ 00 (θn )θn0
uniformly over ξn . Note that, for fixed z,pnψ 00 (θn )θn )) − Φ(0)p→z
φ(0)/( nψ 00 (θn )θn )

Φ(z/(
since
Φ

z
p

!
− Φ(0) = φ(0) pz+ φ0 (ζ)(30)

z22nψ 00 (θn )θn2

nψ 00 (θn )θnnψ 00 (θn )θnp
for some ζ between 0 and z/( nψ 00 (θn )θn ). Also note that

!2 
!
z
z
z
Cz
1 − p
φ p− φ(0) = φ0 (ζ) p≤√nnψ 00 (θn )θnnψ 00 (θn )θnnψ 00 (θn )θnuniformly over z. From (30) and (31), and by dominated convergence, (29) becomes




Z ∞
1
1
1
1
−z
p
ze dz + o √
=p
+o √
n
n
2πnψ 00 (θn )θn 0
2πnψ 00 (θn )θn

(31)

(32)

uniformly over ξn .
As a result, (25) gives (8) for xn ≤ n and (26) gives (9) for xn ≤ . This concludes the theorem.

Lam
ACKNOWLEDGMENTS
The author would like to thank the anonymous referees for their fruitful comments that help improve thepresentation of this paper, and for pointing out some of the references.
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AUTHOR BIOGRAPHIESHENRY LAM is an Assistant Professor in the Department of Mathematics and Statistics at Boston
University. He graduated from Harvard University with a Ph.D. degree in statistics in 2011. His researchinterests lie in applied probability and Monte Carlo methods with applications in queueing, operations
management and insurance modeling.