Given a sequence of fractional frequency deviates, we investigate the relationship between the sample variance of these deviates and the total variance (Totvar) estimator of the Allan variance. We demonstrate that we can recover exactly twice the sample variance by renormalizing the Totvar estimator and then summing it over dyadic averaging times 1, 2, 4, . . . , 2 along with one additional ter...

One may consider a discrete-event simulation as a Markov chain evolving on a suitably rich state space. One way that regenerative cycles may be constructed for general state-space Markov chains is to generate auxiliary coinflip random variables at each transition, with a regeneration occurring if the coin-flip results in a success. The regenerative cycles are therefore randomized with respect t...

Analysis of variance (ANOVA) is the most efficient method available for the analysis of experimental data. Analysis of variance is a method of considerable complexity and subtlety, with many different variations, each of which applies in a particular experimental context. Hence, it is possible to apply the wrong type of ANOVA to data and, therefore, to draw an erroneous conclusion from an exper...

Real-Time (60fps @ 1920x1200) Shadows Produced Using Our Algorithm Variance shadow maps (VSMs) provide an e cient way to produce highquality, ltered shadows in real-time, but su er from light bleeding artifacts. Layered variance shadow maps automatically partition the depth range into multiple layers, which alleviates or eliminates light bleeding artifacts. Compared to VSMs, LVSMs: • Reduce or ...

Non-additive interaction between genetic variants, or epistasis, is a possible explanation for the gap between heritability of complex traits and the variation explained by identified genetic loci. Interactions give rise to genotype dependent variance, and therefore the identification of variance quantitative trait loci can be an intermediate step to discover both epistasis and gene by environm...

Model-free estimates of the noise variance are important for doing model selection and setting tuning parameters. In this paper a data representation is discussed which leads to such an estimator suitable for multidimensional input data. The visual representation called the differogram cloud is based on the 2-norm of the differences of the inputand output-data. A corrected way to estimate the v...

This paper derives a model for the variance in the length and orientation of edge segments extracted from an image. It is assumed that edge pixel positions are subject to noise. The eeect of this positional uncertainty is propagated all the way to segment length and orientation. The model predicts an approximately constant variance in segment length, whereas the variance in segment orientation ...

We propose a new combined estimator, called semiparametric estimator, which incorporates the parametric and nonparametric estimators of the conditional variance in a multiplicative way. We derive bias, variance, and asymptotic normality of the combined estimator. Semiparametric estimators are found to be superior to parametric and nonparametric estimators, both in simulation and empirical analy...

This paper proposes and evaluates variance reduction techniques for efficient estimation of portfolio loss probabilities using Monte Carlo simulation. Precise estimation of loss probabilities is essential to calculating value-at-risk, which is simply a percentile of the loss distribution. The methods we develop build on delta-gamma approximations to changes in portfolio value. The simplest way ...

d : We expect that the wild population will have more variation in body weight than the tankreared population because wild fish will encounter different environments out in the ocean, thus contributing more environmental variance to the total phenotypic variance. Unfortunately, the way I wrote the question, it was not clear which population of fish one should be comparing the phenotypic varianc...