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CERTAIN BOUNDS CONCERNING CHARACTERISTIC FUNCTIONS1
Authors:D J Daley
Institution:Statistics Department (IAS) The Australian National University
Abstract:The largest value of the constant c for which inline image holds over the class of random variables X with non-zero mean and finite second moment, is c=π. Let the random variable (r.v.) X with distribution function F(·) have non-zero mean and finite second moment. In studying a certain random walk problem (Daley, 1976) we sought a bound on the characteristic function inline image of the form inline image for some positive constant c. Of course the inequality is non-trivial only provided that inline image. This note establishes that the best possible constant c =π. The wider relevance of the result is we believe that it underlines the use of trigonometric inequalities in bounding the (modulus of a) c.f. (see e.g. the truncation inequalities in §12.4 of Loève (1963)). In the present case the bound thus obtained is the best possible bound, and is better than the bound (2) |1-?(θ)| ≥ |θEX|-θ2EX2\2 obtained by applying the triangular inequality to the relation inline image which follows from a two-fold integration by parts in the defining equation (*). The treatment of the counter-example furnished below may also be of interest. To prove (1) with c=π, recall that sin u > u(1-u/π) (all real u), so inline image Since |E sinθX|-|E sin(-θX)|, the modulus sign required in (1) can be inserted into (4). Observe that since sin u > u for u < 0, it is possible to strengthen (4) to (denoting max(0,x) by x+) inline image To show that c=π is the best possible constant in (1), assume without loss of generality that EX > 0, and take θ > 0. Then (1) is equivalent to (6) c < θEX2/{EX-|1-?(θ)|/θ} for all θ > 0 and all r.v.s. X with EX > 0 and EX2. Consider the r.v. inline image where 0 < x < 1 and 0 < γ < ∞. Then EX=1, EX2=1+γx2, inline image From (4) it follows that |1-?(θ)| > 0 for 0 < |θ| <π|EX|/EX2 but in fact this positivity holds for 0 < |θ| < 2π|EX|/EX2 because by trigonometry and the Cauchy-Schwartz inequality, |1-?(θ)| > |Re(1-?(θ))| = |E(1-cosθX)| = 2|E sin2θX/2| (10) >2(E sinθX/2)2 (11) >(|θEX|-θ2EX2/2π)2/2 > 0, the inequality at (11) holding provided that |θEX|-θ2EX2/2π > 0, i.e., that 0 < |θ| < 2π|EX|/EX2. The random variable X at (7) with x= 1 shows that the range of positivity of |1-?(θ)| cannot in general be extended. If X is a non-negative r.v. with finite positive mean, then the identity inline image shows that (1-?(θ))/iθEX is the c.f. of a non-negative random variable, and hence (13) |1-?(θ)| < |θEX| (all θ). This argument fans if pr{X < 0}pr{X> 0} > 0, but as a sharper alternative to (14) |1-?(θ)| < |θE|X||, we note (cf. (2) and (3)) first that (15) |1-?(θ)| < |θEX| +θ2EX2/2. For a bound that is more precise for |θ| close to 0, |1-?(θ)|2= (Re(1-?(θ)))2+ (Im?(θ))2 <(θ2EX2/2)2+(|θEX| +θ2EX2-/π)2, so (16) |1-?(θ)| <(|θEX| +θ2EX2-/π) + |θ|3(EX2)2/8|EX|.
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