Perspectives on Mathematical/Statistical Model Building (MASMOB) in research on aging.

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.

Download Full-text

Reassessing the Paradigms of Statistical Model-Building

Oberwolfach Reports ◽

10.4171/owr/2007/50 ◽

2007 ◽

pp. 2943-3012

Author(s):

Ursula Gather ◽

Peter Hall ◽

Hans Rudolf Künsch

Keyword(s):

Statistical Model ◽

Model Building

Download Full-text

Using Genetic Programming in Industrial Statistical Model Building

Genetic Programming Theory and Practice II - Genetic Programming ◽

10.1007/0-387-23254-0_3 ◽

2006 ◽

pp. 31-48 ◽

Cited By ~ 4

Author(s):

Flor Castillo ◽

Arthur Kordon ◽

Jeff Sweeney ◽

Wayne Zirk

Keyword(s):

Genetic Programming ◽

Statistical Model ◽

Model Building

Download Full-text

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

BMC Medical Research Methodology ◽

10.1186/s12874-021-01373-z ◽

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.

Download Full-text