We propose a method for the time-resolved joint analysis of two related aspects of single neuron variability, the spiking irregularity measured by the squared coefficient of variation (CV(2)) of the ISIs and the trial-by-trial variability of the spike count measured by the Fano factor (FF). We provide a calibration of both estimators using the theory of renewal processes, and verify it for spik...

Since the seminal work of Mann and Picard in 1995, the standard way to build high dynamic range (HDR) images from regular cameras has been to combine a reduced number of photographs captured with different exposure times. The algorithms proposed in the literature differ in the strategy used to combine these frames. Several experimental studies comparing their performances have been reported, sh...

We investigate methods for regression analysis when covariates are measured with errors. In a subset of the whole cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies the classical measurement error model, but it may not have repeated measurements. In addition to the surrogate variables that are available among the subjects in the...

Estimating equations have found wide popularity recently in parametric problems, yielding consistent estimators with asymptotically valid inferences obtained via the sandwich formula. Motivated by a problem in nutritional epidemiology, we use estimating equations to derive nonparametric estimators of a \parameter" depending on a predictor. The nonparametric component is estimated via local poly...

Classical extreme value methods were derived when the underlying process is assumed to be a sequence of independent random variables. However when observations are taken along the time and/or the space the independence is an unrealistic assumption. A parameter that arises in this situation, characterizing the degree of local dependence in the extremes of a stationary series, is the extremal ind...

We use N-body-spectrophotometric simulations to investigate the impact of incompleteness and incorrect redshifts in spectroscopic surveys on photometric redshift training and calibration and the resulting effects on cosmological parameter estimation from weak lensing shear–shear correlations. The photometry of the simulations is modelled after the upcoming Dark Energy Survey and the spectroscop...

Parameter estimation is the first step in constructing any control chart. Most estimators of mean and dispersion are sensitive to the presence of outliers. The data may be contaminated by outliers either locally or globally. The exciting robust estimators deal only with global contamination. In this paper a robust estimator for dispersion is proposed to reduce the effect of local contamination ...

The end-to-end Human Mesh Recovery (HMR) approach has been successfully used for 3D body reconstruction. However, most HMR-based frameworks reconstruct human by directly learning mesh parameters from images or videos, while lacking explicit guidance of pose in visual data. As a result, the generated often exhibits incorrect complex activities. To tackle this problem, we propose to exploit calib...

We introduce deep-field metacalibration, a new technique that reduces the pixel noise in metacalibration estimators of weak lensing shear signals by using deeper imaging survey for calibration. In standard when estimating object's response, extra is added to correct effect shearing image, increasing uncertainty on estimates ~ 20%. Our leverages separate, calculate calibrations with less degrada...