Results 261 to 270 of about 1,434,136 (298)

Prediction intervals for growth curves

Journal of Applied Statistics, 1990
The growth curve model Yn×p = An×p ξ mtimes;kBk×p+ Enxp, where Y is an observation matrix, &sigma is a matrix of unknown parameters, A is a known matrix of rank m, B is a known matrix of rank k with 1'= (1, …, 1) as its first row, and the rows of E are independent each distributed as Np(0,Σ,) is considered.
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Predicting lipid sorting in curved membranes

Most biological membranes are curved, and both lipids and proteins play a role in generating curvature. For any given membrane shape and composition, it is not trivial to determine whether lipids are laterally distributed in a homogeneous or inhomogeneous way, and whether the inter-leaflet distribution is symmetric or not.
Jackson, Crowley, Cécile, Hilpert, Luca, Monticelli   +2 more
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Predicting magnetic amplifier control curves

Electrical Engineering, 1952
THE CONTROL CURRENT flowing in a self-saturated magnetic amplifier supplies the magnetomotive force to maintain the specified bias flux density in the core. To predict the output obtained for a given control current the magnetic properties of the core material must be known.
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Comparison of effacement curve with dilatation curve for prediction of labor progression

Journal of Obstetrics and Gynaecology Research, 2017
AbstractAimThis study was conducted to evaluate the ability of the effacement curve to predict fetal descent by comparing it to dilatation in order to improve the accuracy of the current partogram.MethodWe conducted an observational study of women who were admitted for vaginal delivery at Mobini Hospital, Sabzevar, Iran in 2015.
Jila Agah, Roya Baghani, Milad Nazarzadeh, Sedigheh Borna   +3 more
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Clustering and prediction of risk spread curves

Proceedings of the International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing, 2009
In the paper a combination of clustering, detection of risk spread curves in the clusters and prediction is presented. The risk curves are identified and analyzed by two different types of neural networks using supervised and unsupervised learning. Such an analysis allows estimation of future effects about issuers with currently increasing risk in the ...
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Prediction intervals for growth curve forecasts

Journal of Forecasting, 1995
AbstractSince growth curves are often used to produce medium‐ to long‐term forecasts for planning purposes, it is obviously of value to be able to associate an interval with the forecast trend. The problems in producing prediction intervals are well described by Chatfield.
Nigel Meade, Towhidul Islam
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Prediction of long-term creep curves

Fusion Engineering and Design, 1992
Abstract This paper aims at discussing how to predict long-term irradiation enhanced creep properties from shor-term tests. The predictive method based on the π concept was examined by using creep data of ferritic steels. The method was successful in predicting creep curves including the tertiary creep stage as well as rupture lifetimes.
Hiroshi Oikawa, Kouichi Maruyama
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Modeling height-diameter curves for prediction

Canadian Journal of Forest Research, 2015
Individual tree heights are needed in many situations, including estimation of tree volume, dominant height, and simulation of tree growth. However, height measurements are tedious compared to tree diameter measurements, and therefore height–diameter (H–D) models are commonly used for prediction of tree height.
Lauri Mehtätalo, Sergio de-Miguel, Timothy G. Gregoire   +2 more
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Predicting Output Using the Entire Yield Curve

SSRN Electronic Journal, 2011
Many studies find that yields for government bonds predict real economic activity. Most of these studies use the yield spread, defined as the difference between two yields of specific maturities, to predict output. In this paper, I propose a different approach that makes use of information contained in the entire term structure of U.S.
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Noise prediction of trains in curves

2021
People become more and more aware of noise and noise regulation thresholds decrease to satisfy the needs of a quiet environment, while also preventing health issues due to noise pollution.Avoidance of noise as a limiting factor in train operation results in a huge investment regardingnoise mitigation measures, which raises building and maintenance ...
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