Topic
Sample variance
About: Sample variance is a research topic. Over the lifetime, 1636 publications have been published within this topic receiving 68798 citations.
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TL;DR: Findings indicate that low EPV can lead to major problems, and the regression coefficients were biased in both positive and negative directions, and paradoxical associations (significance in the wrong direction) were increased.
6,490 citations
TL;DR: The classic model of the temporal variation of speculative prices (Bachelier 1900) assumes that successive changes of a price Z(t) are independent Gaussian random variables as discussed by the authors.
Abstract: The classic model of the temporal variation of speculative prices (Bachelier 1900) assumes that successive changes of a price Z(t) are independent Gaussian random variables. But, even if Z(t) is replaced by log Z(t),this model is contradicted by facts in four ways, at least:
(1)
Large price changes are much more frequent than predicted by the Gaussian; this reflects the “excessively peaked” (“leptokurtic”) character of price relatives, which has been well-established since at least 1915.
(2)
Large practically instantaneous price changes occur often, contrary to prediction, and it seems that they must be explained by causal rather than stochastic models.
(3)
Successive price changes do not “look” independent, but rather exhibit a large number of recognizable patterns, which are, of course, the basis of the technical analysis of stocks.
(4)
Price records do not look stationary, and statistical expressions such as the sample variance take very different values at different times; this nonstationarity seems to put a precise statistical model of price change out of the question.
4,973 citations
TL;DR: The authors showed that the extra variation due to the presence of these estimated parameters in the weight matrix accounts for much of the difference between the finite sample and the usual asymptotic variance of the two-step generalized method of moments estimator, when the moment conditions used are linear in the parameters.
3,967 citations
TL;DR: In this article, the authors extend the jackknife and the bootstrap method of estimating standard errors to the case where the observations form a general stationary sequence, and they show that consistency is obtained if $l = l(n) \rightarrow \infty$ and $l(n)/n \ rightarrow 0$.
Abstract: We extend the jackknife and the bootstrap method of estimating standard errors to the case where the observations form a general stationary sequence. We do not attempt a reduction to i.i.d. values. The jackknife calculates the sample variance of replicates of the statistic obtained by omitting each block of $l$ consecutive data once. In the case of the arithmetic mean this is shown to be equivalent to a weighted covariance estimate of the spectral density of the observations at zero. Under appropriate conditions consistency is obtained if $l = l(n) \rightarrow \infty$ and $l(n)/n \rightarrow 0$. General statistics are approximated by an arithmetic mean. In regular cases this approximation determines the asymptotic behavior. Bootstrap replicates are constructed by selecting blocks of length $l$ randomly with replacement among the blocks of observations. The procedures are illustrated by using the sunspot numbers and some simulated data.
2,185 citations
TL;DR: Support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals’ brain maturity across development, and prediction of individual brain maturity as a functional connectivity maturation index is allowed.
Abstract: Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals' brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain's major functional networks.
1,886 citations