Enhancing Predictive Accuracy in Pharmaceutical Sales Through An Ensemble Kernel Gaussian Process Regression Approach

• URL: http://arxiv.org/abs/2404.19669v1
• Date: Mon, 15 Apr 2024 00:25:10 GMT
• Title: Enhancing Predictive Accuracy in Pharmaceutical Sales Through An Ensemble Kernel Gaussian Process Regression Approach
• Authors: Shahin Mirshekari, Mohammadreza Moradi, Hossein Jafari, Mehdi Jafari, Mohammad Ensaf,
• Abstract summary: This research employs Gaussian Process Regression (GPR) with an ensemble kernel to analyze pharmaceutical sales data. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an ( R2) score near 1.0, and significantly lower values in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.
• Score: 0.20971479389679337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Mat\'ern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Mat\'ern, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an \( R^2 \) score near 1.0, and significantly lower values in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.

Related papers

• Integrating Marketing Channels into Quantile Transformation and Bayesian Optimization of Ensemble Kernels for Sales Prediction with Gaussian Process Models [0.0]
  This study introduces an innovative Gaussian Process (GP) model utilizing an ensemble kernel that integrates Radial Basis Function (RBF), Rational Quadratic, and Mat'ern kernels for product sales forecasting. By applying Bayesian optimization, we efficiently find the optimal weights for each kernel, enhancing the model's ability to handle complex sales data patterns. Our approach significantly outperforms traditional GP models, achieving a notable 98% accuracy and superior performance across key metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination ($R2
  arXiv  Detail & Related papers  (2024-04-15T00:11:01Z)
• Gaussian Process Regression under Computational and Epistemic Misspecification [4.5656369638728656]
  In large data applications, computational costs can be reduced using low-rank or sparse approximations of the kernel. This paper investigates the effect of such kernel approximations on the element error.
  arXiv  Detail & Related papers  (2023-12-14T18:53:32Z)
• Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels [78.6096486885658]
  We introduce lower bounds to the linearized Laplace approximation of the marginal likelihood. These bounds are amenable togradient-based optimization and allow to trade off estimation accuracy against computational complexity.
  arXiv  Detail & Related papers  (2023-06-06T19:02:57Z)
• Optimization of Annealed Importance Sampling Hyperparameters [77.34726150561087]
  Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. We present a parameteric AIS process with flexible intermediary distributions and optimize the bridging distributions to use fewer number of steps for sampling. We assess the performance of our optimized AIS for marginal likelihood estimation of deep generative models and compare it to other estimators.
  arXiv  Detail & Related papers  (2022-09-27T07:58:25Z)
• Sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm [62.997667081978825]
  We propose a computationally efficient and powerful Bayesian approach for sparse high-dimensional linear regression. Minimal prior assumptions on the parameters are used through the use of plug-in empirical Bayes estimates. The proposed approach is implemented in the R package probe.
  arXiv  Detail & Related papers  (2022-09-16T19:15:50Z)
• Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning [48.08663378234329]
  Kernel-based models such as kernel ridge regression and Gaussian processes are ubiquitous in machine learning applications. Existing sparse approximation methods can yield a significant reduction in the computational cost. We provide novel confidence intervals for the Nystr"om method and the sparse variational Gaussian processes approximation method.
  arXiv  Detail & Related papers  (2022-02-08T17:22:09Z)
• How Good are Low-Rank Approximations in Gaussian Process Regression? [28.392890577684657]
  We provide guarantees for approximate Gaussian Process (GP) regression resulting from two common low-rank kernel approximations. We provide experiments on both simulated data and standard benchmarks to evaluate the effectiveness of our theoretical bounds.
  arXiv  Detail & Related papers  (2021-12-13T04:04:08Z)
• Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning [54.01682318834995]
  Preconditioning is a highly effective step for any iterative method involving matrix-vector multiplication. We prove that preconditioning has an additional benefit that has been previously unexplored. It simultaneously can reduce variance at essentially negligible cost.
  arXiv  Detail & Related papers  (2021-07-01T06:43:11Z)
• How Good are Low-Rank Approximations in Gaussian Process Regression? [24.09582049403961]
  We provide guarantees for approximate Gaussian Process (GP) regression resulting from two common low-rank kernel approximations. We provide experiments on both simulated data and standard benchmarks to evaluate the effectiveness of our theoretical bounds.
  arXiv  Detail & Related papers  (2020-04-03T14:15:10Z)
• SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives [86.01677297601624]
  We propose a novel approach for scaling GP regression with derivatives based on quadrature Fourier features. We prove deterministic, non-asymptotic and exponentially fast decaying error bounds which apply for both the approximated kernel as well as the approximated posterior.
  arXiv  Detail & Related papers  (2020-03-05T14:33:20Z)
• Kernel Selection for Modal Linear Regression: Optimal Kernel and IRLS Algorithm [8.571896191090744]
  We show that a Biweight kernel is optimal in the sense of minimizing an mean squared error of a resulting MLR parameter. Secondly, we provide a kernel class for which algorithm iteratively reweighted least-squares algorithm (IRLS) is guaranteed to converge.
  arXiv  Detail & Related papers  (2020-01-30T03:57:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.