Convexity Bias Estimate

• Convexity bias estimate is defined as the systematic overestimation error arising when convex functions are applied to noisy data, as explained by Jensen’s inequality.
• Bias correction methods, including bootstrap-based shifting, multiplicative scaling, and covariance-based adjustments, aim to reduce the mean squared error of convex estimators.
• These techniques have practical applications in entropy estimation, high-dimensional regression, and penalized inference, supported by theoretical guarantees under regularity conditions.

Convexity bias estimate refers to a suite of formal methodologies for quantifying and correcting the systematic estimation error introduced when evaluating convex functions or functionals on noisy or random arguments. This bias emerges due to Jensen’s inequality whenever convexity is present: for a convex $F:\Omega\subset\mathbb{R}^d\to\mathbb{R}$ and any distribution $\mu$ with mean $x^*$, $F(x^*)\le \mathbb{E}[F(x)]$. Thus, when estimating $F(x^*)$ using noisy data, naive plug-in estimators systematically overshoot, with their expected value strictly above the target, resulting in convexity bias. This phenomenon and its correction are central in statistical estimation, inference for random objective functions, high-dimensional regression with convex regularizers, and functional estimation in the analysis of random distributions (Ma et al., 2022, Bellec et al., 2019).

1. Definition of Convexity Bias

Let $\{x_i\}_{i=1}^n$ be i.i.d. samples from a distribution $\mu$ on $\Omega \subset \mathbb{R}^d$, where $x^* = \mathbb{E}_\mu[x]$, and $F$ is convex. Forming the empirical mean $\mu$0, the standard plug-in estimator is $\mu$1. By Jensen’s inequality,

$\mu$2

with bias defined as

$\mu$3

This bias is inherent whenever the convexity of $\mu$4 interacts with the randomness of $\mu$5, regardless of the specifics of $\mu$6 or the function, as long as $\mu$7 is strictly convex.

2. Frameworks for Bias Correction

Two principal methodologies are developed for convexity bias correction: (i) generic debiasing via bootstrap-based shifts or scaling, and (ii) analytic covariance-based correction if $\mu$8 is sufficiently smooth and its Hessian is accessible (Ma et al., 2022). The aim is to produce an estimator $\mu$9 with provably smaller mean squared error (MSE) relative to $x^*$0.

2.1 Additive Shifting

Construct an additive adjustment $x^*$1 to minimize

$x^*$2

with the (infeasible) optimal shift $x^*$3. As $x^*$4 and $x^*$5 are unknown, the bootstrap is used:

• Generate $x^*$6 resampled means $x^*$7 from bootstrapped datasets.
• Form

$x^*$8

yielding the debiased estimator $x^*$9.

2.2 Multiplicative Scaling

For nonnegative convex $F(x^*)\le \mathbb{E}[F(x)]$0, a multiplicative scale $F(x^*)\le \mathbb{E}[F(x)]$1 is chosen to minimize

$F(x^*)\le \mathbb{E}[F(x)]$2

where the optimal $F(x^*)\le \mathbb{E}[F(x)]$3. Bootstrap approximates this as

$F(x^*)\le \mathbb{E}[F(x)]$4

and outputs $F(x^*)\le \mathbb{E}[F(x)]$5.

2.3 Covariance-Based Shift

If the Hessian $F(x^*)\le \mathbb{E}[F(x)]$6 is known or can be (cheaply) estimated, a second-order Taylor expansion yields

$F(x^*)\le \mathbb{E}[F(x)]$7

where $F(x^*)\le \mathbb{E}[F(x)]$8. The empirical analog,

$F(x^*)\le \mathbb{E}[F(x)]$9

provides a fast, analytic bias correction.

3. Theoretical Guarantees and Regularity

The main theoretical results establish strict improvement in MSE for the debiased estimators under regularity:

• $F(x^*)$0 must have bounded derivatives up to fourth order.
• $F(x^*)$1 must possess sufficient moment conditions (up to 8th moment for shifting, all moments for scaling).
• For scaling, $F(x^*)$2.

Specifically, for sufficiently large $F(x^*)$3, using either the bootstrap shift or scale with $F(x^*)$4,

$F(x^*)$5

Proof strategies center on Taylor expansion of $F(x^*)$6 about $F(x^*)$7, control of higher-order terms, and analysis of MSE coefficients (Ma et al., 2022).

4. Algorithms and Computational Aspects

The primary algorithms differ in their requirements and cost:

• Shift-Bootstrap: $F(x^*)$8 (where $F(x^*)$9 is the evaluation cost of $\{x_i\}_{i=1}^n$0).
• Scale-Bootstrap: identical to shift, with substituted computation of $\{x_i\}_{i=1}^n$1.
• Covariance-Shift: $\{x_i\}_{i=1}^n$2 if Hessian is dense.

Moderate values of $\{x_i\}_{i=1}^n$3 (typically $\{x_i\}_{i=1}^n$4–$\{x_i\}_{i=1}^n$5 for $\{x_i\}_{i=1}^n$6 in moderate ranges) suffice in practice to realize MSE improvements without excessive computational overhead.

Method	Key Requirement	Computational Cost
Shift-Bootstrap	No structure; only $\{x_i\}_{i=1}^n$7-evals required	$\{x_i\}_{i=1}^n$8
Scale-Bootstrap	$\{x_i\}_{i=1}^n$9	Same as shift
Covariance-Shift	Hessian $\mu$0 accessible	$\mu$1 for dense $\mu$2

5. Applications and Special Cases

The convexity bias estimation framework is applicable in a broad range of settings:

• Entropy Estimation: For $\mu$3 (concave on probability simplex), the covariance-shift retrieves classic Miller's bias correction as a special case, with

$\mu$4

Bootstrap corrections offer further improvements, particularly for non-uniform distributions (Ma et al., 2022).

• 2-Wasserstein Distance: For $\mu$5 defined as the minimum expected squared distance over couplings, bias-corrected estimators follow from the same bootstrap resampling paradigm.
• Convex-Penalized High-Dimensional Regression: Regularization bias, a different manifestation of convexity bias, arises in convex-penalized estimators (e.g., Lasso, Group Lasso) due to the penalty term. De-biasing via Stein’s formula and the accompanying MSE and CLT results enable construction of valid confidence intervals and risk bounds (Bellec et al., 2019).

6. Relation to Regularization Bias and High-Dimensional Inference

Convexity bias is distinct but related to regularization bias in penalized estimation. In penalized estimators $\mu$6, the KKT condition reveals a bias term induced by the penalty gradient. Recent de-biasing strategies for such settings derive from Stein’s formula and permit not only bias correction but also the derivation of asymptotic normality and confidence intervals for linear contrasts. These “de-biased” or “desparsified” estimators are uniformly valid in both low- and high-dimensional regimes, for broad classes of convex penalties, further cementing the central role of convexity bias estimation in modern statistics (Bellec et al., 2019).

7. Limitations, Scope, and Practical Guidelines

The convexity bias estimation frameworks apply to any convex (or concave) $\mu$7 for which function evaluation is feasible. No further structure is required. However:

• There are no minimax optimality guarantees relative to problem-specific estimators.
• Performance depends on the smoothness of $\mu$8 and the moment properties of $\mu$9.
• For best results, (i) prefer covariance-based shift when a Hessian is cheaply available (entropy, quadratic loss); (ii) otherwise, use bootstrap shifting with $\Omega \subset \mathbb{R}^d$0; (iii) for positive $\Omega \subset \mathbb{R}^d$1, bootstrap scaling is viable.
• Practitioners should verify that debiased estimators reduce MSE in pilot studies and always report both naive and debiased estimates to assess improvement and residual bias (Ma et al., 2022).

A plausible implication is that convexity bias estimation constitutes a foundational correction step for any statistical procedure where convex functions or functionals are evaluated on random or noisy arguments, with immediate application to estimation in random optimization, distributional statistics, and penalized inference.

References

• "Correcting Convexity Bias in Function and Functional Estimate" (Ma et al., 2022)
• "De-biasing convex regularized estimators and interval estimation in linear models" (Bellec et al., 2019)