Tests against Nonconstancy in Linear Models Based on Counting Statistics

1989 ◽  
pp. 53-70
Author(s):  
Peter Hackl ◽  
Walter Katzenbeisser
Keyword(s):  
Linear Models ◽  
Counting Statistics

Radiation Damage, Contrast and Statistics in High Resolution Biological Electron Microscopy

1970 ◽  
Vol 28 ◽  
pp. 260-261
Author(s):  
Robert M. Glaeser
Keyword(s):  
High Resolution ◽  
Particle Flux ◽  
Unit Area ◽  
Signal To Noise ◽  
Resolution Image ◽  
High Resolution Image ◽  
Pass Through ◽  
Counting Statistics ◽  
The Relationship ◽  
Picture Element

It is well known that a large flux of electrons must pass through a specimen in order to obtain a high resolution image while a smaller particle flux is satisfactory for a low resolution image. The minimum particle flux that is required depends upon the contrast in the image and the signal-to-noise (S/N) ratio at which the data are considered acceptable. For a given S/N associated with statistical fluxtuations, the relationship between contrast and “counting statistics” is s131_eqn1, where C = contrast; r2 is the area of a picture element corresponding to the resolution, r; N is the number of electrons incident per unit area of the specimen; f is the fraction of electrons that contribute to formation of the image, relative to the total number of electrons incident upon the object.

Applied Linear Models with SAS

2009 ◽  
Author(s):  
Daniel Zelterman
Keyword(s):  
Linear Models

Improved inference in linear models with additional information

Optimization ◽  
1975 ◽  
Vol 6 (5) ◽  
pp. 817-829
Author(s):  
O. Bunke
Keyword(s):  
Linear Models ◽  
Additional Information

A Note on Effects of Digital Approximation in Linear Models

1974 ◽  
Vol 3 (9) ◽  
pp. 893-897
Author(s):  
Gerald McWilliams ◽  
James Poirot†
Keyword(s):  
Linear Models

Effects of Momentary Affect on Satisfaction Judgments

2020 ◽  
Vol 41 (2) ◽  
pp. 61-67
Author(s):  
Marko Tončić ◽  
Petra Anić
Keyword(s):  
Linear Models ◽  
Time Frame ◽  
Hierarchical Linear Models ◽  
Curvilinear Relationship ◽  
Positive And Negative Affect ◽  
Transient Events ◽  
Time Frames ◽  
Events Data ◽  
Level Satisfaction ◽  
Source Of Information

Abstract. This study aims to examine the effect of affect on satisfaction, both at the between- and the within-person level for momentary assessments. Affect is regarded as an important source of information for life satisfaction judgments. This affective effect on satisfaction is well established at the dispositional level, while at the within-person level it is heavily under-researched. This is true especially for momentary assessments. In this experience sampling study both mood and satisfaction scales were administered five times a day for 7 days via hand-held devices ( N = 74 with 2,122 assessments). Several hierarchical linear models were fitted to the data. Even though the amount of between-person variance was relatively low, both positive and negative affect had substantial effects on momentary satisfaction on the between- and the within-person level as well. The within-person effects of affect on satisfaction appear to be more pronounced than the between-person ones. At the momentary level, the amount of between-person variance is lower than in studies with longer time-frames. The affect-related effects on satisfaction possibly have a curvilinear relationship with the time-frame used, increasing in intensity up to a point and then decreasing again. Such a relationship suggests that, at the momentary level, satisfaction might behave in a more stochastic manner, allowing for transient events/data which are not necessarily affect-related to affect it.

Introduction to Hierarchical Linear Models

1994 ◽  
Vol 39 (5) ◽  
pp. 475-476
Author(s):  
Paula L. Woehlke
Keyword(s):  
Linear Models ◽  
Hierarchical Linear Models

G Optimal Design in Non linear Models to Increase Silicon Oxide Purity Levels and Electrical Conductivity

2018 ◽  
pp. 150-155
Author(s):  
Muklas Rivai
Keyword(s):  
Electrical Conductivity ◽  
Optimal Design ◽  
Linear Models ◽  
Silicon Oxide ◽  
Information Matrix ◽  
Relevant Information ◽  
Non Linear

Optimal design is a design which required in determining the points of variable factors that would be attempted to optimize the relevant information so that fulfilled the desired criteria. The optimal fulfillment criteria based on the information matrix of the selected model.

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