Add to ORKGWe point out that models based on probability theory, and the statistical techniques derived from them, have limited applicability, at least in exploratory multivariate situations. Prior knowledge, if avail- able, must be incorporated into the analysis to yield greater stability. If prior knowledge is not available, however, it must not be invented. This applies to both the structural and the replication framework aspects of a model. The methods of gauging and stability analysis are introduced as alternatives. They use the notion of a technique as the pivot of data anal- ysis, not that of a model. Homogeneity analysis is used as an example
Models derived from scientific considerations often exhibit four characteristics: (1) a likelihood i...
Despite discussions about the replicability of findings in psychological research, two issues have b...
The paper deals with discrete state transition models in the context of exploratory research, where ...
We point out that models based on probability theory, and the statistical techniques derived from th...
In the situations typically encountered in the social sciences the methodology of traditional stati...
This paper presents a novel methodological approach called the Model of Models (MoM). MoM concerns t...
summary:Multivariate models frequently used in many branches of science have relatively large number...
In this book problems related to the choice of models in such diverse fields as regression, covarian...
Often scientific information on various data generating processes are presented in the from of numer...
This paper explores the robustness of conclusions from a statistical model against variations in mod...
This paper presents a classification of statistical models using a simple and logical framework. Som...
R. A. Fisher founded modern statistical inference in 1922 and identified its fundamental problems to...
We argue that model selection uncertainty should be fully incorporated into statistical inference wh...
Analysis of data sets that involve large numbers of variables usually entails some type of model fit...
In regression analysis, a special and most attention must be paid on assumption validating the model...
Models derived from scientific considerations often exhibit four characteristics: (1) a likelihood i...
Despite discussions about the replicability of findings in psychological research, two issues have b...
The paper deals with discrete state transition models in the context of exploratory research, where ...
We point out that models based on probability theory, and the statistical techniques derived from th...
In the situations typically encountered in the social sciences the methodology of traditional stati...
This paper presents a novel methodological approach called the Model of Models (MoM). MoM concerns t...
summary:Multivariate models frequently used in many branches of science have relatively large number...
In this book problems related to the choice of models in such diverse fields as regression, covarian...
Often scientific information on various data generating processes are presented in the from of numer...
This paper explores the robustness of conclusions from a statistical model against variations in mod...
This paper presents a classification of statistical models using a simple and logical framework. Som...
R. A. Fisher founded modern statistical inference in 1922 and identified its fundamental problems to...
We argue that model selection uncertainty should be fully incorporated into statistical inference wh...
Analysis of data sets that involve large numbers of variables usually entails some type of model fit...
In regression analysis, a special and most attention must be paid on assumption validating the model...
Models derived from scientific considerations often exhibit four characteristics: (1) a likelihood i...
Despite discussions about the replicability of findings in psychological research, two issues have b...
The paper deals with discrete state transition models in the context of exploratory research, where ...