scholarly journals Volume and variance in the linear statistical model

2002 ◽  
Vol 357 (1-3) ◽  
pp. 303-306 ◽  
Author(s):  
I.C. Araújo ◽  
M.P. de Oliveira
1986 ◽  
Vol 17 (4-5) ◽  
pp. 237-250 ◽  
Author(s):  
P. Allerup ◽  
H. Madsen

The paper discusses a bi-linear statistical model for correcting aerodynamic errors, earlier presented by Allerup and Madsen, 1980. Further data from Finland, USA and Australia testing the model will be presented. A simplification of the bi-linear model in order to cover different gauge types and varying measuring levels will be demonstrated. The simplification will extend the applicability of the correction model when implemented into automatic correction systems. The paper will discuss the problems of fit by the simplified model and attention will be given to physical interpretation of the mathematical structure in the model. Besides aerodynamic errors, wetting losses influence the correction values. It will be demonstrated how these effects cause too large corrections for small amounts of precipitation and too small corrections for large amounts.


1987 ◽  
Vol 12 (3) ◽  
pp. 225-233 ◽  
Author(s):  
J. Gary Lutz ◽  
Leigh A. Cundari

After a hypothesis about some linear statistical model has been tested and rejected (e.g., in an ANOVA), many researchers employ the Scheffe procedure to locate the source(s) of the rejection. This procedure guarantees that there is at least one linear combination of the model parameters (consistent with the hypothesis) that is significantly different from its hypothesized value. This most significant parametric function is not always easy to find, however, because it may not manifest itself in simple functions (such as pairwise contrasts between groups) or in “obvious” functions (such as those suggested by the graph of an interaction). A general solution to this problem is presented along with a practical example of its application.


NeuroImage ◽  
1996 ◽  
Vol 3 (3) ◽  
pp. S102 ◽  
Author(s):  
Robert Turner ◽  
Karl Friston ◽  
John Ashburner ◽  
Oliver Josephs ◽  
Alistair Howseman

2016 ◽  
Vol 29 (3) ◽  
pp. 1091-1107 ◽  
Author(s):  
Samson M. Hagos ◽  
Zhe Feng ◽  
Casey D. Burleyson ◽  
Chun Zhao ◽  
Matus N. Martini ◽  
...  

Abstract Two Madden–Julian oscillation (MJO) episodes observed during the 2011 Atmospheric Radiation Measurement Program MJO Investigation Experiment (AMIE)/DYNAMO field campaign are simulated using a regional model with various cumulus parameterizations, a regional cloud-permitting model, and a global variable-resolution model with a high-resolution region centered over the tropical Indian Ocean. Model biases in relationships relevant to existing instability theories of MJO are examined and their relative contributions to the overall model errors are quantified using a linear statistical model. The model simulations capture the observed approximately log-linear relationship between moisture saturation fraction and precipitation, but precipitation associated with the given saturation fraction is overestimated especially at low saturation fraction values. This bias is a major contributor to the excessive precipitation during the suppressed phase of MJO. After accounting for this bias using a linear statistical model, the spatial and temporal structures of the model-simulated MJO episodes are much improved, and what remains of the biases is strongly correlated with biases in saturation fraction. The excess precipitation bias during the suppressed phase of the MJO episodes is accompanied by excessive column-integrated radiative forcing and surface evaporation. A large portion of the bias in evaporation is related to biases in wind speed, which are correlated with those of precipitation. These findings suggest that the precipitation bias sustains itself at least partly by cloud radiative feedbacks and convection–surface wind interactions.


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