DT
D.A. Tudor
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
1 records found
1
The present work focuses on constructing predictive densities, conditional on a set of features, for one-dimensional real-valued random variables. We approach the problem in a model-agnostic manner, aiming for methods that can be applied to arbitrary models without imposing parametric assumptions on the underlying distribution.
We first present the standard kernel density estimation approach and discuss its limitations. In this context, the conformal framework, originally developed for prediction intervals, is particularly appealing due to its finite-sample marginal validity guarantees. We then introduce conformal predictive distributions, a recent development in the literature that ensures the associated predictive system is marginally Unif[0,1] under the Probability Integral Transform (PIT).
However, these distributions tend to be highly fuzzy. As a result, directly applying finite differencing to conformal predictive distributions produces noisy predictive densities that may obscure important underlying features.
To address this issue, we propose two solutions. First, Gaussian filtering yields the smoothest densities and empirically maintains PIT perturbations within a satisfactory range, although no theoretical bounds on the perturbation are derived. Second, we introduce a new method, termed quantile-matching, which produces less fuzzy densities while providing a sharp theoretical upper bound on PIT perturbation.
Furthermore, we show that when the number of quantiles is allowed to equal the size of the calibration set, the distribution induced by quantile-matching coincides with the crisp modification of conformal predictive distributions, thereby yielding an upper bound on their PIT perturbation as well.
Finally, we evaluate the proposed methods on a large simulated real estate transactions dataset based on the Hierarchical Trend Model. Our results indicate that the quantile-matching approach outperforms competing methods across several metrics, including the Mean Absolute Error of the associated tail means and running time per transaction price. ...
We first present the standard kernel density estimation approach and discuss its limitations. In this context, the conformal framework, originally developed for prediction intervals, is particularly appealing due to its finite-sample marginal validity guarantees. We then introduce conformal predictive distributions, a recent development in the literature that ensures the associated predictive system is marginally Unif[0,1] under the Probability Integral Transform (PIT).
However, these distributions tend to be highly fuzzy. As a result, directly applying finite differencing to conformal predictive distributions produces noisy predictive densities that may obscure important underlying features.
To address this issue, we propose two solutions. First, Gaussian filtering yields the smoothest densities and empirically maintains PIT perturbations within a satisfactory range, although no theoretical bounds on the perturbation are derived. Second, we introduce a new method, termed quantile-matching, which produces less fuzzy densities while providing a sharp theoretical upper bound on PIT perturbation.
Furthermore, we show that when the number of quantiles is allowed to equal the size of the calibration set, the distribution induced by quantile-matching coincides with the crisp modification of conformal predictive distributions, thereby yielding an upper bound on their PIT perturbation as well.
Finally, we evaluate the proposed methods on a large simulated real estate transactions dataset based on the Hierarchical Trend Model. Our results indicate that the quantile-matching approach outperforms competing methods across several metrics, including the Mean Absolute Error of the associated tail means and running time per transaction price. ...
The present work focuses on constructing predictive densities, conditional on a set of features, for one-dimensional real-valued random variables. We approach the problem in a model-agnostic manner, aiming for methods that can be applied to arbitrary models without imposing parametric assumptions on the underlying distribution.
We first present the standard kernel density estimation approach and discuss its limitations. In this context, the conformal framework, originally developed for prediction intervals, is particularly appealing due to its finite-sample marginal validity guarantees. We then introduce conformal predictive distributions, a recent development in the literature that ensures the associated predictive system is marginally Unif[0,1] under the Probability Integral Transform (PIT).
However, these distributions tend to be highly fuzzy. As a result, directly applying finite differencing to conformal predictive distributions produces noisy predictive densities that may obscure important underlying features.
To address this issue, we propose two solutions. First, Gaussian filtering yields the smoothest densities and empirically maintains PIT perturbations within a satisfactory range, although no theoretical bounds on the perturbation are derived. Second, we introduce a new method, termed quantile-matching, which produces less fuzzy densities while providing a sharp theoretical upper bound on PIT perturbation.
Furthermore, we show that when the number of quantiles is allowed to equal the size of the calibration set, the distribution induced by quantile-matching coincides with the crisp modification of conformal predictive distributions, thereby yielding an upper bound on their PIT perturbation as well.
Finally, we evaluate the proposed methods on a large simulated real estate transactions dataset based on the Hierarchical Trend Model. Our results indicate that the quantile-matching approach outperforms competing methods across several metrics, including the Mean Absolute Error of the associated tail means and running time per transaction price.
We first present the standard kernel density estimation approach and discuss its limitations. In this context, the conformal framework, originally developed for prediction intervals, is particularly appealing due to its finite-sample marginal validity guarantees. We then introduce conformal predictive distributions, a recent development in the literature that ensures the associated predictive system is marginally Unif[0,1] under the Probability Integral Transform (PIT).
However, these distributions tend to be highly fuzzy. As a result, directly applying finite differencing to conformal predictive distributions produces noisy predictive densities that may obscure important underlying features.
To address this issue, we propose two solutions. First, Gaussian filtering yields the smoothest densities and empirically maintains PIT perturbations within a satisfactory range, although no theoretical bounds on the perturbation are derived. Second, we introduce a new method, termed quantile-matching, which produces less fuzzy densities while providing a sharp theoretical upper bound on PIT perturbation.
Furthermore, we show that when the number of quantiles is allowed to equal the size of the calibration set, the distribution induced by quantile-matching coincides with the crisp modification of conformal predictive distributions, thereby yielding an upper bound on their PIT perturbation as well.
Finally, we evaluate the proposed methods on a large simulated real estate transactions dataset based on the Hierarchical Trend Model. Our results indicate that the quantile-matching approach outperforms competing methods across several metrics, including the Mean Absolute Error of the associated tail means and running time per transaction price.