Variance Modeling in ASReml

2017 ◽  
pp. 87-106 ◽  
Author(s):  
Fikret Isik ◽  
James Holland ◽  
Christian Maltecca
Keyword(s):  
Variance Modeling

Speech Emotion Verification Using Emotion Variance Modeling and Discriminant Scale-Frequency Maps

2015 ◽  
Vol 23 (10) ◽  
pp. 1552-1562 ◽  
Author(s):  
Jia-Ching Wang ◽  
Yu-Hao Chin ◽  
Bo-Wei Chen ◽  
Chang-Hong Lin ◽  
Chung-Hsien Wu
Keyword(s):  
Variance Modeling

FRACTIONAL BROWNIAN MOTION WITH STOCHASTIC VARIANCE: MODELING ABSOLUTE RETURNS IN STOCK MARKETS

2008 ◽  
Vol 19 (08) ◽  
pp. 1221-1242 ◽  
Author(s):  
H. E. ROMAN ◽  
M. PORTO
Keyword(s):  
Brownian Motion ◽  
Fractional Brownian Motion ◽  
Stock Markets ◽  
Distribution Functions ◽  
Conditional Heteroskedasticity ◽  
Arch Models ◽  
Variance Modeling ◽  
Probability Distribution Functions ◽  
Second Moments ◽  
Long Time

We discuss a model for simulating a long-time memory in time series characterized in addition by a stochastic variance. The model is based on a combination of fractional Brownian motion (FBM) concepts, for dealing with the long-time memory, with an autoregressive scheme with conditional heteroskedasticity (ARCH), responsible for the stochastic variance of the series, and is denoted as FBMARCH. Unlike well-known fractionally integrated autoregressive models, FBMARCH admits finite second moments. The resulting probability distribution functions have power-law tails with exponents similar to ARCH models. This idea is applied to the description of long-time autocorrelations of absolute returns ubiquitously observed in stock markets.

Data Normalization for Engine Health Monitoring

2005 ◽  
Author(s):  
Robert J. Bronson ◽  
Hans R. Depold ◽  
Ravi Rajamani ◽  
Somnath Deb ◽  
William H. Morrison ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Input Parameter ◽  
Error Variance ◽  
Parameter Estimates ◽  
Variance Modeling ◽  
Detection Algorithms ◽  
Fuel Flow ◽  
Modeling Errors ◽  
Model Engine ◽  
Parameter Correction

In this paper we present a systematic data-driven parameter correction and estimation process consisting of outlier detection and removal, relevant input parameter selection, advanced statistical and empirical correlation, and prediction fusion to reduce variance in relevant engine parameter estimates. We model engine parameter deviations from nominal, and show that these methods can result in significant reductions in bias and variance modeling errors. Reducing the error variance increases the signal-to-noise ratio, thereby increasing the reliability and speed of fault-detection algorithms. The overall objective function is to reduce the measurement variances without masking faults. Key parameters modeled include fuel flow, rotor speed(s), and measured temperatures.

scholarly journals Incorporation of Biologic Response Variance Modeling Into the Clinic: Limiting Risk of Brachial Plexopathy and Other Late Effects of Breast Cancer Proton Beam Therapy

2020 ◽  
Vol 10 (2) ◽  
pp. e71-e81 ◽  
Author(s):  
Robert W. Mutter ◽  
Krishan R. Jethwa ◽  
Hok Seum Wan Chan Tseung ◽  
Stephanie M. Wick ◽  
Mohamed M.H. Kahila ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Proton Beam ◽  
Late Effects ◽  
Proton Beam Therapy ◽  
Brachial Plexopathy ◽  
Variance Modeling ◽  
Response Variance ◽  
Biologic Response

scholarly journals Realized Variance Modeling: Decoupling Forecasting from Estimation*

2020 ◽  
Vol 18 (3) ◽  
pp. 532-555
Author(s):  
Fabrizio Cipollini ◽  
Giampiero M Gallo ◽  
Alessandro Palandri
Keyword(s):  
Long Range ◽  
Long Range Dependence ◽  
Realized Variance ◽  
Variance Modeling ◽  
Out Of Sample ◽  
Arma Modeling ◽  
Better Than

Abstract This article evaluates the in-sample fit and out-of-sample forecasts of various combinations of realized variance models and functions delivering estimates (estimation criteria). Our empirical findings highlight that: independently of the econometrician’s forecasting loss (FL) function, certain estimation criteria perform significantly better than others; the simple ARMA modeling of the log realized variance generates superior forecasts than the Heterogeneous Autoregressive (HAR) family, for any of the FL functions considered; the (2, 1) parameterizations with negative lag-2 coefficient emerge as the benchmark specifications generating the best forecasts and approximating long-range dependence as does the HAR family.

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