Local Primordial Non-Gaussianities and super-sample variance

Viable counts of bacteria are often high in some areas and low in adjacent areas of the same surface of fresh meat. The present study indicated that rubbing meat surfaces together before sampling reduces variation among bacterial plate counts of pieces of beef plate meat. Counts before rubbing ranged from 2 to 6,187/cm2, whereas counts after rubbing ranged from 15 to 2,043/cm2. The reduced sample variance allowed for fewer samples to be taken in studies of cleaning and sanitizing of fresh beef.

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Definitions of Sample Variance: Some Teaching Problems to be Overcome

Journal of the Royal Statistical Society Series D (The Statistician) ◽

10.2307/2348636 ◽

1992 ◽

Vol 41 (1) ◽

pp. 55 ◽

Cited By ~ 2

Author(s):

Hardeo Sahai ◽

Satish Misra

Keyword(s):

Sample Variance

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The Regression of the Sample Variance on the Sample Mean

Journal of the London Mathematical Society ◽

10.1112/jlms/s1-21.1.22 ◽

1946 ◽

Vol s1-21 (1) ◽

pp. 22-28 ◽

Cited By ~ 12

Author(s):

M. C. K. Tweedie

Keyword(s):

Sample Variance ◽

Sample Mean

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Limit Theory for High Frequency Sampled MCARMA Models

Advances in Applied Probability ◽

10.1239/aap/1409319563 ◽

2014 ◽

Vol 46 (3) ◽

pp. 846-877 ◽

Cited By ~ 3

Author(s):

Vicky Fasen

Keyword(s):

Asymptotic Behavior ◽

High Frequency ◽

Lévy Process ◽

Asymptotic Properties ◽

Domain Of Attraction ◽

Levy Process ◽

Sample Variance ◽

Limit Theory ◽

Second Moments ◽

Time Grid

We consider a multivariate continuous-time ARMA (MCARMA) process sampled at a high-frequency time grid {hn, 2hn,…, nhn}, where hn ↓ 0 and nhn → ∞ as n → ∞, or at a constant time grid where hn = h. For this model, we present the asymptotic behavior of the properly normalized partial sum to a multivariate stable or a multivariate normal random vector depending on the domain of attraction of the driving Lévy process. Furthermore, we derive the asymptotic behavior of the sample variance. In the case of finite second moments of the driving Lévy process the sample variance is a consistent estimator. Moreover, we embed the MCARMA process in a cointegrated model. For this model, we propose a parameter estimator and derive its asymptotic behavior. The results are given for more general processes than MCARMA processes and contain some asymptotic properties of stochastic integrals.

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