Abstract. Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average) models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average) models for seasonal streamflow series). However, with McLeod-Li test and Engle’s Lagrange Multiplier test, clear evidences are found for t...

Genetic association studies have explained only a small proportion of the estimated heritability of complex traits, leaving the remaining heritability "missing." Genetic interactions have been proposed as an explanation for this, because they lead to overestimates of the heritability and are hard to detect. Whether this explanation is true depends on the proportion of variance attributable to g...

Aim of this study was to estimate variance components for litter size in Improved Jezersko-Solcava sheep. Analysis involved 66,082 records from 12,969 animals, for the number of lambs born in all parities (BA), the fi rst parity (B1), and later parities (B2+). Fixed part of model contained the eff ects of season and age at lambing within parity. Random part of model contained the eff ects of he...

A statistician designing an experiment wants to get as much information as possible from the data gathered. Often this means the most precise estimate possible (that is, an estimate with minimum possible variance) of the unknown parameters. If there are several parameters, this can be interpreted in many ways: do we want to minimize the average variance, or the maximum variance, or the volume o...

We propose a jackknife variance estimator for the population average from two, two-phase samples after imputation. The jackknife method has long been used to estimate and reduce bias, but has now become a valuable tool for variance estimation. We apply two different sampling methods, (simple random sampling and stratified random sampling) to derive jackknife variance estimators for the twosampl...

A generalized minimum variance controller is developed for servo application for multiple input and multiple output systems having time-varying dynamics. The plant to be controlled is a controlled autoregressive moving average model and the control objective is to minimize a generalized minimum variance performance index that is an extension of the standard index from linear time invariant case...

We introduce $k$-variance, a generalization of variance built on the machinery random bipartite matchings. $K$-variance measures expected cost matching two sets $k$ samples from distribution to each other, capturing local rather than global information about measure as increases; it is easily approximated stochastically using sampling and linear programming. In addition defining $k$-variance pr...

This paper investigates variance risk premia in energy commodities, particularly crude oil and natural gas, using a robust model-independent approach. Over a period of 11 years, we find that the average variance risk premia are significantly negative for both energy commodities. However, it is difficult to explain the level and variation in energy variance risk premia with systematic or commodi...

background: the present study was designed to ascertain serial changes on distal radius radiographic parameters attributable to aging.       methods: in this prospective study, the sample consisted of 120 healthy individuals who were divided into four age groups each containing 15 males and 15 females. in the two below-20-year-old groups, only ulnar variance could be investigated. wrist radiogr...