This article investigates a problem for statistical model evaluation, in particular for curve fitting: by employing a different family of curves we can fit any scatter plot almost perfectly at apparently minor cost in terms of model complexity. The problem is resolved by an appeal to prior probabilities. This leads to some general lessons about how to approach model evaluation
In this paper, we introduce a new concept for constructing prior distributions. We exploit the natur...
To most applied statisticians, a fitting procedure’s degrees of freedom is syn-onymous with its mode...
We investigate the structure of model selection problems via the bias/variance decomposition. In par...
This article investigates a problem for statistical model evaluation, in particular for curve fittin...
This paper investigates a problem for statistical model evaluation, in particular for curve fitting:...
This article investigates a problem for statistical model evaluation, in particular for curve fittin...
Defining and quantifying complexity is one of the major challenges of modern science and contemporar...
No statistical model is right or wrong, true or false in a strict sense. We only evaluate and compar...
We consider a multivariate model with independent marginals as a benchmark for a generic multivariat...
In the last few decades, model complexity has received a lot of press. While many methods have been ...
In this article, we introduce the concept of model uncertainty. We review the frequentist and Bayesi...
Model selection plays an important part in machine learning and in artificial intelligence in genera...
To select among competing generative models of timeseries data, it is necessary to balance the goodn...
Key words and phrases. Complexity regularization, classi cation, pattern recognition, regression est...
The size and complexity of Simulink models is constantly increasing, just as the systems which they ...
In this paper, we introduce a new concept for constructing prior distributions. We exploit the natur...
To most applied statisticians, a fitting procedure’s degrees of freedom is syn-onymous with its mode...
We investigate the structure of model selection problems via the bias/variance decomposition. In par...
This article investigates a problem for statistical model evaluation, in particular for curve fittin...
This paper investigates a problem for statistical model evaluation, in particular for curve fitting:...
This article investigates a problem for statistical model evaluation, in particular for curve fittin...
Defining and quantifying complexity is one of the major challenges of modern science and contemporar...
No statistical model is right or wrong, true or false in a strict sense. We only evaluate and compar...
We consider a multivariate model with independent marginals as a benchmark for a generic multivariat...
In the last few decades, model complexity has received a lot of press. While many methods have been ...
In this article, we introduce the concept of model uncertainty. We review the frequentist and Bayesi...
Model selection plays an important part in machine learning and in artificial intelligence in genera...
To select among competing generative models of timeseries data, it is necessary to balance the goodn...
Key words and phrases. Complexity regularization, classi cation, pattern recognition, regression est...
The size and complexity of Simulink models is constantly increasing, just as the systems which they ...
In this paper, we introduce a new concept for constructing prior distributions. We exploit the natur...
To most applied statisticians, a fitting procedure’s degrees of freedom is syn-onymous with its mode...
We investigate the structure of model selection problems via the bias/variance decomposition. In par...
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