Numerical algebraic fan of a design for statistical model building

2016 ◽  
Author(s):  
Nikolaus Rudak ◽  
Sonja Kuhnt ◽  
Eva Riccomagno
Keyword(s):  
Statistical Model ◽  
Model Building

Building a Mathematical Model to Predict Transient Drilling Temperature Responses

1969 ◽  
Vol 91 (3) ◽  
pp. 641-651 ◽  
Author(s):  
U. K. Saxena ◽  
S. M. Wu
Keyword(s):  
Mathematical Model ◽  
Statistical Model ◽  
Functional Form ◽  
Model Building ◽  
Design Procedure ◽  
Drilling Temperature ◽  
Confirmatory Tests ◽  
Temperature Responses ◽  
Theoretical Considerations ◽  
Building Technique

A transient drilling temperature predicting equation is developed using a statistical model-building technique. The functional form of the model is obtained by combining both theoretical considerations and an empirical approach. The parameters of this model are estimated and improved using a sequential procedure in designing the experiments. Confirmatory tests show that the model describes the transient drilling temperature responses remarkably well. The effectiveness of the design procedure for obtaining the “best” estimates of the parameters is also demonstrated.

Reassessing the Paradigms of Statistical Model-Building

2007 ◽  
pp. 2943-3012
Author(s):  
Ursula Gather ◽  
Peter Hall ◽  
Hans Rudolf Künsch
Keyword(s):  
Statistical Model ◽  
Model Building

scholarly journals Statistical model building: Background “knowledge” based on inappropriate preselection causes misspecification

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lorena Hafermann ◽  
Heiko Becher ◽  
Carolin Herrmann ◽  
Nadja Klein ◽  
Georg Heinze ◽  
...  
Keyword(s):  
Statistical Model ◽  
Model Building ◽  
Background Knowledge ◽  
Explanatory Models ◽  
Study Data ◽  
Data Sets ◽  
Knowledge Based ◽  
Selection Of Variables ◽  
Open Question ◽  
Selection Of

Abstract Background Statistical model building requires selection of variables for a model depending on the model’s aim. In descriptive and explanatory models, a common recommendation often met in the literature is to include all variables in the model which are assumed or known to be associated with the outcome independent of their identification with data driven selection procedures. An open question is, how reliable this assumed “background knowledge” truly is. In fact, “known” predictors might be findings from preceding studies which may also have employed inappropriate model building strategies. Methods We conducted a simulation study assessing the influence of treating variables as “known predictors” in model building when in fact this knowledge resulting from preceding studies might be insufficient. Within randomly generated preceding study data sets, model building with variable selection was conducted. A variable was subsequently considered as a “known” predictor if a predefined number of preceding studies identified it as relevant. Results Even if several preceding studies identified a variable as a “true” predictor, this classification is often false positive. Moreover, variables not identified might still be truly predictive. This especially holds true if the preceding studies employed inappropriate selection methods such as univariable selection. Conclusions The source of “background knowledge” should be evaluated with care. Knowledge generated on preceding studies can cause misspecification.

Statistical Model Building and Model Criticism for Human Circadian Data

1999 ◽  
Vol 14 (6) ◽  
pp. 609-616 ◽  
Author(s):  
Emery N. Brown ◽  
Harry Luithardt
Keyword(s):  
Statistical Model ◽  
Model Building
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