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Marshall lemma in discrete convex estimation

Fadoua Balabdaoui, Cécile Durot

To cite this version:

Fadoua Balabdaoui, Cécile Durot. Marshall lemma in discrete convex estimation. Statistics and Probability Letters, Elsevier, 2015, �10.1016/j.spl.2015.01.016�. �hal-01250085�

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Marshall Lemma in discrete convex estimation

Fadoua Balabdaoui

1

and C´

ecile Durot

2∗

January 16, 2015

1CEREMADE, Universit´e Paris-Dauphine, 75775, CEDEX 16, Paris, France 2UFR SEGMI, Universit´e Paris Ouest Nanterre La D´efense, F-92001, Nanterre, France

Abstract

We show that the supremum distance between the cumulative distribution of the convex LSE and an arbitrary distribution function F with a convex pmf on N is at most twice the supremum distance between the empirical distribution function and F .

Keywords: convex, nonparametric least squares, Marshall lemma, pmf, shape

con-straints

1

Introduction

1.1

A brief overview

The first Marshall’s inequality goes back to Marshall (1970). It states that if bFnis the least

concave majorant (LCM) of the empirical distribution function Fn, then k bFn−F k∞ ≤ kFn−

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F k∞ for an arbitrary concave distribution function F . Here, all the distributions involved

are supported on [0, ∞). The proof of this inequality is rather elementary and uses basic

properties of the LCM. When F is the true distribution function with a decreasing density

f supported on a compact real interval and assumed to be continuously differentiable with

a strictly negative derivative, Kiefer and Wolfowitz (1976) showed that the global rate

of convergence of k bFn − Fnk∞ is of order n−2/3log(n) almost surely. In convex density

estimation, two Marshall-type of inequalities have been established for the convex least

squares estimator (LSE) of a density on [0, ∞) defined by Groeneboom et al. (2001). Let

F be an arbitrary distribution function on [0, ∞) such that F0 is convex, Fn the empirical

distribution function, bFn the cumulative distribution of the LSE, bHn =

0Fbn(s)ds, Hn =

0Fn(s)ds and H =

0F (s)ds respectively. Then, D¨umbgen et al. (2007) and Balabdaoui

and Rufibach (2008) showed that k bFn−F k∞ ≤ 2 kFn−F k∞, and k bHn−Hk∞ ≤ kHn−Hk∞

respectively. Those results were used by Balabdaoui and Wellner (2007) to show

Kiefer-Wolfowitz-type of inequalities. Specifically, they show that when F is the true distribution

such that f = F0 is twice continuously differentiable with f00 > 0, k bFn− Fnk∞ and k bHn−

Hnk∞ are respectively of order (n−1log(n))3/5 and (n−1log(n))4/5.

1.2

Discrete versus continuous

In both the monotone and convex estimation problems, the Kiefer-Wolfowitz-type of

in-equalities recalled above clearly suggest that if one is ready to assume that the true density

or its derivative does not admit any flat part on its support, then the empirical and

con-strained estimators for the unknown distribution function are equivalent for n large enough.

In the discrete setting, this is not true anymore simply because the notion of a strict

cur-vature does not exist in this case. In fact, with Fn the empirical distribution and bFn the

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(2014), it can be shown with similar arguments as for the proof of Theorem 3.2 in

Balab-daoui et al. (2014), that ( bFn, Fn) converges jointly, at the rate

n, to a quite complicated

limiting distribution, so that √n( bFn− Fn) converges to a distribution that we conjecture

is not degenerate. Hence, it is somehow expected that the difference between bFn and Fn

converges exactly at the rate 1/√n in this case. Although we do not pursue this in this short note, an immediate consequence of our main theorem is that this difference is at

most Op(1/

n) in the supremum norm. This theorem gives the same form of the

Mar-shall inequality proved by D¨umbgen et al. (2007) for the convex LSE in the continuous

setting. Although we re-adapt their idea of using concavity of the difference between the

LSE and the density of F (with respect to the counting measure in our case) between two

successive knot points a < b, our proof does not rely on the equalities Fn(a) = bFn(a) and

Fn(b) = bFn(b) as they are no longer true for the discrete LSE. One has also to deal with

dis-crete sums instead of integrals, which makes the final inequalities a bit less straightforward

to obtain. One of the consequences of this note is to be able to assert that the convergence

rate of bFn to the truth F is the expected rate 1/

n, whether F has a finite support or not.

In Balabdaoui et al. (2014), the assumption that F is finitely supported on {0, . . . , S} for

some unknown integer S > 0 was made to be able to establish the limiting distribution of

the LSE, the general case being much more complex to handle. However, this assumption

was not at all necessary for Durot et al. (2013) to obtain that kpbn− pkk = Op(1/

√ n) for

all k ∈ [2, ∞], wherepbnand p are respectively the convex discrete LSE and the true

proba-bility mass function (pmf). However, for k = ∞, the transition from this result to showing

that k bFn − F k∞ = Op(1/

n) is not immediate unless F admits a finite support. The

Marshall lemma established in this note alleviates any existing doubt that the parametric

convergence rate holds true independently of the nature of the support of the true convex

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2

Marshall inequality

Based on a n-sample from a convex discrete probability mass function (pmf) on N, let b

pn denote the discrete convex LSE of the pmf as defined by Durot et al. (2013). We

recall that the points in N\{0} where pbn changes its slopes are called knots, and that the characterization of pbn is given by z−1 X x=0 b Fn(x)        ≥Pz−1 x=0Fn(x), z ∈ N\{0} =Pz−1 x=0Fn(x), if z is a knot of pbn, (2.1)

where Fn is the empirical distribution function and bFn is the distribution function

cor-responding to bpn, see Proposition 2.1 in Balabdaoui et al. (2014). In the sequel, F is a

distribution function on N with corresponding pmf, p, that is decreasing and convex on N. We denote by Kn the set consisting of the point 0 and all knots of pbn and by ¯pn the empirical pmf, that is the pmf corresponding to Fn. We start with the following result.

Lemma 2.1 For τ ∈ Kn, we have bFn(τ − 1) ≤ Fn(τ − 1) and 0 ≤ bFn(τ ) − Fn(τ ) ≤

b

pn(τ ) − ¯pn(τ ).

Proof. First, suppose that τ ≥ 2. From the characterization in (2.1), it follows that

τ −1 X k=0 b Fn(k) = τ −1 X k=0 Fn(k) (2.2) τ −2 X k=0 b Fn(k) ≥ τ −2 X k=0 Fn(k) (2.3)

Then, (2.2) − (2.3) yields bFn(τ − 1) ≤ Fn(τ − 1). Likewise, (2.2) combined to (2.1) with

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under the alternative form bFn(τ ) ≤ Fn(τ ) +pbn(τ ) − ¯pn(τ ), this gives the result for τ ≥ 2.

If τ = 1, then the equality in (2.2) gives the first claimed inequality of the lemma

b

Fn(0) = Fn(0). Combined with the inequality in (2.1) with z = 2, this yields the second

claimed inequality of the lemma, that is, Fn(1) ≤ bFn(1) = Fn(1) +pbn(1) − ¯pn(1).

Finally, if τ = 0, then bFn(−1) = Fn(−1) = 0 and (2.1) with z = 1 yields 0 ≤ bFn(0) −

Fn(0) = bpn(0) − ¯pn(0) which completes the proof of the Lemma.  In what follows, we will establish two important inequalities linking the extrema of

b Fn− F to those of Fn− F . Proposition 2.2 We have max x∈N b Fn(x) − F (x) ≤ 2 max x∈N Fn(x) − F (x) (2.4) and min x∈N b Fn(x) − F (x) ≥ −2 max x∈N Fn(x) − F (x) . (2.5)

Proof. Put D = bFn− F and d = bpn− p. We begin with the proof of (2.4).

Assume first that the supremum of D over N is achieved at some m ≥bsn, where bsn is the greatest point in the support of bpn. Note that bsn+ 1 is the largest knot of pbn. It follows from Theorem 1 of Durot et al. (2013) thatbsn≥ max(X1, . . . , Xn), and bFn(x) = Fn(x) = 1

for all x ≥bsn, and hence

sup x∈N b Fn(x) − F (x)  = sup x≥bsn 1 − F (x) ≤ sup x∈N Fn(x) − F (x)

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implying the inequality in (2.4). In the sequel, we assume that the supremum is achieved at

some m <bsnand we denote by τ1 and τ2 two successive points in Knsuch that τ1 ≤ m < τ2.

If m = τ2− 1, then by the first inequality of Lemma 2.1 we can write

b Fn(m) − F (m) ≤ Fn(m) − F (m) ≤ sup x∈N Fn(x) − F (x)

implying (2.4). Thus, we assume in the sequel that the maximum of D over N is achieved at some m ∈ {τ1, . . . , τ2 − 2} for some consecutive points τ1 and τ2 in Kn that satisfy

τ2 ≥ τ1+ 2. Since the inequality in (2.4) is trivial in the case where D(m) ≤ 0, we assume

furthermore that D(m) > 0. Since D(−1) = 0, it follows from the definition of m that

D(m) ≥ D(m + 1) and D(m) ≥ D(m − 1), which in turn implies that

b

pn(m) ≥ p(m) and pbn(m + 1) ≤ p(m + 1). (2.6)

Consider now the piecewise linear function ˜d defined on the real interval [τ1, τ2] such that

˜

d = d on the set of integers {τ1, . . . , τ2} and which linearly interpolates ˜d between the

integers. The inequalities in (2.6) and continuity of ˜d imply that there exists a real number

m0 ∈ [m, m + 1] such that ˜d(m0) = 0. Note that since p is a convex pmf and pbn is linear on {τ1, . . . , τ2}, ˜d is concave on [τ1, τ2].

Using the same idea as in D¨umbgen et al. (2007), consider the auxiliary linear function ˜

d defined on the real interval [τ1, τ2] such that

˜ d(m0) = 0, and τ2 X x=m+1 ˜ d(x) = D(τ2) − D(m). (2.7)

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Thus ˜d(x) = α(x − m0), where the slope α is given by α = PD(ττ2 2) − D(m) x=m+1(x − m0) = 2(D(τ2) − D(m)) (τ2− m)(τ2+ m + 1 − 2m0) .

Since ˜d − ˜d is concave on [m0, τ2] with a zero at m0, it follows that it can only bear a unique

sign change on that interval. Therefore, its discretized version d − ¯d such that ¯d is defined

as ¯d(x) = ˜d(x) = α(x − m0) on the set of integers {m + 1, . . . , τ2} can only bear a unique

sign change on that set. This in turn implies that

z

X

x=m+1

d(x) − ¯d(x) ≥ 0, for all z ∈ {m + 1, . . . , τ2}. (2.8)

To see this, note that there exists z0 ∈ [m0, τ2] such that ˜d − ˜d ≥ 0 on [m0, z0] and < 0

on (z0, τ2]. It follows that d(x) − ¯d(x) ≥ 0 if x ∈ {m + 1, . . . , bz0c} and d(x) − ¯d(x) < 0 if

x ∈ {bz0c + 1, . . . , τ2}, where btc denotes the integer part of a real number t. Hence, the

inequality in (2.8) holds true for z ∈ {m + 1, . . . , bz0c}. Now, the second equality in (2.7)

implies that if z ∈ {bz0c + 1, . . . , τ2− 1}, then

z X x=m+1 d(x) − ¯d(x) = − τ2 X x=z+1 d(x) − ¯d(x) > 0; and if z = τ2, then Pz

x=m+1 d(x)− ¯d(x) = 0. Thus, we can write for all z ∈ {m+1, . . . , τ2}

D(z) − D(m) ≥ z X x=m+1 ¯ d(x) = α z X x=m+1 (x − m0) or equivalently D(z) ≥ D(m) + (D(τ2) − D(m)) (z − m)(z + m + 1 − 2m0) (τ2− m)(τ2+ m + 1 − 2m0) . (2.9)

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On the other hand, taking the difference between the equality and inequality of the

char-acterization in (2.1) at the points z = τ2 and z = m + 1 respectively we can write

τ2−1 X x=m+1 D(x) ≤ τ2−1 X x=m+1 Fn(x) − F (x) 

which, combined with (2.9), implies that

τ2−1 X x=m+1 Fn(x) − F (x) ≥ (τ2− 1 − m)D(m) + D(τ2) − D(m)S (2.10) where S = Pτ2−1 z=m+1(z − m)(z − m + 1) − 2(m0− m) Pτ2−1 z=m+1(z − m) (τ2− m)(τ2+ m + 1 − 2m0) = Pτ2−1−m k=1 k 2+Pτ2−1−m k=1 k − 2(m0− m) Pτ2−1−m k=1 k (τ2− m)(τ2+ m + 1 − 2m0) = 1 3(τ2 − 1 − m)(τ2− m)(τ2 − m − 1 2) + (τ2− 1 − m)(τ2− m) 1 2 − (m0− m)  (τ2− m)(τ2+ m + 1 − 2m0) = 1 3(τ2− 1 − m) × τ2− m + 1 − 3t τ2− m + 1 − 2t , where t = m0− m ∈ [0, 1].

Since D(τ2) − D(m) ≤ 0 by definition of m, we want to find an upper bound for S in

t ∈ [0, 1]. To make the notation even lighter, let us put η = τ2 − m + 1. Note that

m ≤ τ2− 2 by assumption so that η > 2 and η − 2t > 0 for all t ∈ [0, 1]. A quick study of

the variations of the function t 7→ (η − 3t)/(η − 2t), for t ∈ [0, 1] shows that it attains its

maximal value at 0 and that the maximal value is equal to 1. Hence, S ≤ (τ2 − 1 − m)/3

so that the inequality in (2.10) implies that

τ2−1 X x=m+1 Fn(x) − F (x) ≥ (τ2− 1 − m)D(m) + D(τ2) − D(m) 3 (τ2− 1 − m).

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Now, using the fact that τ2−1 X x=m+1 Fn(x) − F (x) ≤ (τ2− 1 − m) max x∈N Fn(x) − F (x)  it follows that D(m) ≤ 3 2maxx∈N Fn(x) − F (x) − 1 2D(τ2). (2.11)

Combining the second statement of Lemma 2.1 with (2.11) yields

D(m) ≤ 3 2maxx∈N Fn(x) − F (x) + 1 2maxx∈N Fn(x) − F (x) ≤ 2 max x∈N Fn(x) − F (x) ,

which completes the proof of (2.4).

Now we turn to the proof of (2.5). Similar to the proof of (2.4), it is easy to see that

(2.5) holds if the minimum of D is achieved at some m ≥ bsn. Therefore, it suffices to

prove (2.5) for the case where the minimum of D over N is achieved at some m < bsn. As done above, we denote by τ1 and τ2 two successive points in Kn such that τ1 ≤ m < τ2. If

m = τ1, then it follows from the second statement of Lemma 2.1 that

b Fn(m) − F (m) ≥ Fn(m) − F (m) ≥ − max x∈N Fn(x) − F (x) (2.12)

implying (2.5). Thus, we assume in the sequel that the minimum of D over N is achieved at some m ∈ {τ1 + 1, . . . , τ2} for some consecutive points τ1 and τ2 in Kn that satisfy

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imply that

b

pn(m) ≤ p(m) and pbn(m + 1) ≥ p(m + 1). (2.13)

Consider now the piecewise linear function ˜d defined on [τ1, τ2] such that ˜d = d on the set of

integers {τ1, . . . , τ2} and which linearly interpolates d between the integers. The inequalities

in (2.13) and continuity of ˜d imply that there exists a real number m0 ∈ [m, m + 1] such

that ˜d(m0) = 0. As in the proof of (2.4), consider again the auxiliary linear function ˜d

defined on [τ1, τ2] such that

˜ d(m0) = 0, and m X x=τ1 ˜ d(x) = D(m) − D(τ1− 1). Thus ˜d(x) = α(x − m0), where α = 2(D(m) − D(τ1− 1)) (m − τ1+ 1)(τ1+ m − 2m0) .

Using the fact that ˜d− ˜d is concave on the real interval [τ1, τ2] with a zero at m0 ∈ [m, m+1],

it follows that its discretized version d − ¯d can only bear a unique sign change on the set

of integers {τ1, . . . , m}. This in turn implies that we have

m

X

x=z

d(x) − ¯d(x) ≥ 0, for all z ∈ {τ1, . . . , m}.

Thus, we can write for all z ∈ {τ1, . . . , m}

D(m) − D(z − 1) ≥ (D(m) − D(τ1− 1))

(m − z + 1)(m + z − 2m0)

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or equivalently

D(y) ≤ D(m) − (D(m) − D(τ1− 1))

(m − y)(m + y + 1 − 2m0)

(m − τ1+ 1)(m + τ1− 2m0)

(2.14)

for y ∈ {τ1− 1, . . . , m − 1}. On the other hand, taking the difference between the equality

and inequality of the characterization in (2.1) at the points z = τ1 and z = m respectively

implies that m−1 X x=τ1 D(x) ≥ m−1 X x=τ1 Fn(x) − F (x) 

which combined with (2.14) yields

m−1 X x=τ1 Fn(x) − F (x) ≤ (m − τ1)D(m) − D(m) − D(τ1− 1)S (2.15) where S = 1 3(m − τ1) η + 3t η + 1 + 2t ≤ 1 3(m − τ1)

with η = m − τ1− 1 and t = m0− m ∈ [0, 1]. Now, using the fact that

m−1 X x=τ1 Fn(x) − F (x) ≥ (m − τ1) min x∈N Fn(x) − F (x)  it follows that min x∈N Fn(x) − F (x)  ≤ D(m) −1 3(D(m) − D(τ1− 1)) = 2 3D(m) + 1 3D(τ1− 1) ≤ 2 3D(m) + 1 3(Fn(τ1− 1) − F (τ1 − 1))

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where the last inequality is an immediate consequence of the first equality of Lemma 2.1. Hence, D(m) ≥ 3 2minx∈N Fn(x) − F (x) − 1 2(Fn(τ1− 1) − F (τ1− 1)) and (2.5) follows. 

In what follows, we use kqk∞ to denote supx∈N|q(x)| for a function q defined on N. We

now state the Marshall lemma for the convex discrete estimator.

Theorem 2.3 For any distribution function F defined on N whose probability mass func-tion is convex on N, we have k bFn− F k∞ ≤ 2 kFn− F k∞.

Proof. The claimed inequality is an immediate consequence of Proposition 2.2 and the

fact that kqk∞ = maxx∈Nq(x) ∨ maxx∈N(−q(x)) = maxx∈Nq(x) ∨ (− minx∈Nq(x)). 

References

Balabdaoui, F., Durot, C. and Koladjo, F. (2014). On asymptotics of the discrete convex lse of a pmf. arXiv preprint arXiv:1404.3094 .

Balabdaoui, F. and Rufibach, K. (2008). A second Marshall inequality in convex estimation. Statist. Probab. Lett. 78 118–126.

URL http://dx.doi.org/10.1016/j.spl.2007.05.009

Balabdaoui, F. and Wellner, J. A. (2007). A Kiefer-Wolfowitz theorem for convex densities. In Asymptotics: particles, processes and inverse problems, vol. 55 of IMS

Lecture Notes Monogr. Ser. Inst. Math. Statist., Beachwood, OH, 1–31.

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D¨umbgen, L., Rufibach, K. and Wellner, J. A. (2007). Marshalls lemma for convex density estimation. In Asymptotics: Particles, Processes and Inverse Problems. Institute

of Mathematical Statistics, 101–107.

Durot, C., Huet, S., Koladjo, F. and Robin, S. (2013). Least-squares estimation of a convex discrete distribution. Comput. Statist. Data Anal. 67 282–298.

URL http://dx.doi.org/10.1016/j.csda.2013.04.019

Groeneboom, P., Jongbloed, G. and Wellner, J. A. (2001). Estimation of a convex function: characterizations and asymptotic theory. Ann. Statist. 29 1653–1698.

Kiefer, J. and Wolfowitz, J. (1976). Asymptotically minimax estimation of concave and convex distribution functions. Z. Wahrscheinlichkeitstheorie und Verw. Gebiete 34

73–85.

Marshall, A. W. (1970). Discussion of Barlow and van Zwet’s paper. In Nonparametric techniques in Statistical Inference, vol. 1969 of Proceedings of the First International

Symposium on Nonparametric Techniques held at Indiana University, June. Cambridge

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