1 Motivations In machine learning, the standard measure of accuracy for models is the prediction error (PE), i.e. the expected loss on future examples. We consider here the i.i.d. regression or classification setups, where future examples are assumed to be independently sampled from the distribution that generated the training set. When the data distribution is unknown, PE cannot be computed. T...

This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumption is that the mean value is piecewise constant in time, and the task is to ...

In some distributions, such as the binomial distribution, the variance is determined by the mean. However, in practice, overdispersion is often observed where the variance is larger than that predicated by the mean, and underdispersion is sometimes observed where the variance is smaller. It is well known that overdispersion or underdispersion cannot be modeled for dichotomous responses having a...

We obtain V-optimal designs, which minimize the average variance of predicted regression responses, over a finite set of possible regressors. We assume a general and possibly heterogeneous variance structure depending on the design points. The variances are either known (or at least reliably estimated) or unknown. For the former case we exhibit optimal static designs; our methods are then modif...

Variance estimation after imputation is an important practical problem in survey sampling. When deterministic imputation or stochastic imputation is used, we show that the variance of the imputed estimator can be consistently estimated by a unifying linearize and reverse approach. We provide some applications of the approach to regression imputation, fractional categorical imputation, multiple ...

Plasma alkaline phosphatase activity has been measured in 204 pairs of twins aged 18-34. Estimated genetic variance was the same in men and women, after exclusion of the youngest men, but environmental variance was greater in men so that the heritability was .90 +/- .02 in the women and .71 +/- .05 in the 19-34-year-old men. About 15% of the genetic variance was associated with the ABO blood gr...

In breeding programs, robustness of animals and uniformity of end product can be improved by exploiting genetic variation in residual variance. Residual variance can be defined as environmental variance after accounting for all identifiable effects. The aims of this study were to estimate genetic variance in residual variance of body weight, and to estimate genetic correlations between body wei...

For the LSB steganography, a fusion method is proposed to fuse two typical quantitative steganalysis methods based on support vector regression (SVR). This paper first gives some main factors influencing the errors of structural steganalysis and weighted stego image steganalysis, viz. the local variance and saturation. Then, the estimated embedding ratios of above two methods, the local varianc...

In this contribution, variance properties of L2 model reduction are studied. That is, given an estimated model of high order we study the resulting variance of an L2 reduced approximation. The main result of the paper is showing that estimating a low order output error (OE) model via L2 model reduction of a high order model gives a smaller variance compared to estimating a low order model direc...

This paper demonstrates that basic statistics (mean, variance) of the logarithm of the variate itself can be used in the calculation of differential entropy among random variables known to be multiples and powers of a common underlying variate. For the same set of distributions, the variance of the differential self-information is shown also to be a function of statistics of the logarithmic var...