Optimal M-estimation in high-dimensional regression.

We consider, in the modern setting of high-dimensional statistics, the classic problem of optimizing the objective function in regression using M-estimates when the error distribution is assumed to be known. We propose an algorithm to compute this optimal objective function that takes into account the dimensionality of the problem. Although optimality is achieved under assumptions on the design matrix that will not always be satisfied, our analysis reveals generally interesting families of dimension-dependent objective functions.

On robust regression with high-dimensional predictors.

We study regression M-estimates in the setting where p, the number of covariates, and n, the number of observations, are both large, but p ≤ n. We find an exact stochastic representation for the distribution of ß = argmin(ß∈ℝ(p)) Σ(i=1)(n) ρ(Y(i) - X(i')ß) at fixed p and n under various assumptions on the objective function ρ and our statistical model. A scalar random variable whose deterministic limit rρ(κ) can be studied when p/n → κ > 0 plays a central role in this representation. We discover a nonlinear system of two deterministic equations that characterizes rρ(κ). Interestingly, the system shows that rρ(κ) depends on ρ through proximal mappings of ρ as well as various aspects of the statistical model underlying our study. Several surprising results emerge. In particular, we show that, when p/n is large enough, least squares becomes preferable to least absolute deviations for double-exponential errors.

Ranking TEM cameras by their response to electron shot noise.

We demonstrate two ways in which the Fourier transforms of images that consist solely of randomly distributed electrons (shot noise) can be used to compare the relative performance of different electronic cameras. The principle is to determine how closely the Fourier transform of a given image does, or does not, approach that of an image produced by an ideal camera, i.e. one for which single-electron events are modeled as Kronecker delta functions located at the same pixels where the electrons were incident on the camera. Experimentally, the average width of the single-electron response is characterized by fitting a single Lorentzian function to the azimuthally averaged amplitude of the Fourier transform. The reciprocal of the spatial frequency at which the Lorentzian function falls to a value of 0.5 provides an estimate of the number of pixels at which the corresponding line-spread function falls to a value of 1/e. In addition, the excess noise due to stochastic variations in the magnitude of the response of the camera (for single-electron events) is characterized by the amount to which the appropriately normalized power spectrum does, or does not, exceed the total number of electrons in the image. These simple measurements provide an easy way to evaluate the relative performance of different cameras. To illustrate this point we present data for three different types of scintillator-coupled camera plus a silicon-pixel (direct detection) camera.

The Missing Censoring Indicator Model and the Smoothed Bootstrap.

For right censored data with missing censoring indicators, sub-density function kernel estimators play a significant role for estimating a survival function. Data-driven bandwidths for computing these kernel estimators are proposed. The bandwidths are obtained as minimizers of certain estimates of the mean integrated squared error (MISE). It is shown that the smoothed bootstrap offers a motivation for choosing the proposed MISE estimates for minimization. The efficacy of the proposed procedures is investigated through simulation studies and some illustrations are provided.