In this paper, we propose an iterative algorithm to perform model selection. This algorithm is a sequential technique that utilizes ideas from filtering theory. We found that the value of initial parameters in our method plays a key role in selecting models. We study the effect of those parameters, and propose guidelines on how to choose them. Finally, we explore and discuss extensions of the algorithm
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
Model selection is ubiquitous as we simply do not know the underlying data generating process. Howev...
We outline a range of criteria for evaluating model selection approaches that have been used in the ...
this paper is to provide such a comparison, and more importantly, to describe the general conclusion...
In the era of big data, analysts usually explore various statistical models or machine-learning meth...
This paper proposes a new approach for model selection and applies it to a classical time series mod...
A good model is a model that encapsulates the initial process and therefore represents a close estim...
Before using a parametric model one has to be sure that it offers a reasonable description of the sy...
Model selection methods provide a way to select one model among a set of models in a statistically v...
Abstract: State filters can be used to produce online estimates of the state of a process. If an exa...
In this paper, we present a graphical method for selection of the model among the many competitive m...
textabstractModel selection can involve several variables and selection criteria. A simple method to...
This paper evaluates the properties of a joint and sequential estimation procedure for estimating th...
The classical approach to statistical analysis is usually based upon finding values for model parame...
Many popular methods of model selection involve minimizing a penalized function of the data (such as...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
Model selection is ubiquitous as we simply do not know the underlying data generating process. Howev...
We outline a range of criteria for evaluating model selection approaches that have been used in the ...
this paper is to provide such a comparison, and more importantly, to describe the general conclusion...
In the era of big data, analysts usually explore various statistical models or machine-learning meth...
This paper proposes a new approach for model selection and applies it to a classical time series mod...
A good model is a model that encapsulates the initial process and therefore represents a close estim...
Before using a parametric model one has to be sure that it offers a reasonable description of the sy...
Model selection methods provide a way to select one model among a set of models in a statistically v...
Abstract: State filters can be used to produce online estimates of the state of a process. If an exa...
In this paper, we present a graphical method for selection of the model among the many competitive m...
textabstractModel selection can involve several variables and selection criteria. A simple method to...
This paper evaluates the properties of a joint and sequential estimation procedure for estimating th...
The classical approach to statistical analysis is usually based upon finding values for model parame...
Many popular methods of model selection involve minimizing a penalized function of the data (such as...
We here introduce a novel classification approach adopted from the nonlinear model identification fr...
Model selection is ubiquitous as we simply do not know the underlying data generating process. Howev...
We outline a range of criteria for evaluating model selection approaches that have been used in the ...