Age Curvature in a Campaign-Level Selection Surface
Abstract
The question concerns age curvature conditional on recorded campaign characteristics. The secondary ordinary least squares analysis contains 45,211 observations and uses HC3 covariance. The coefficient on age is -0.0168, with a 95 percent interval from -0.0193 to -0.0143. Squared age asks whether a constant age slope adequately summarizes the file conditional on campaign features. It does not identify a causal age or contact effect. Unknown-variance constrained selection would need its own compatible measurement model and terminal requirements. A flexible projection can inform that design without evaluating the adaptive procedure itself. The estimates describe the released collection and its conditioning set. No new data collection, simulator experiment, or deployment-policy evaluation is claimed.
References
L. Yang, S. Gao, C. Li, and Y. Wang, “Stochastically Constrained Best Arm Identification with Thompson Sampling,” Automatica, vol. 176, 112223, 2025.
J. Du, S. Gao, and I. Ryzhov, “Revisiting an Open Question in Ranking and Selection under Unknown Variances,” Proceedings of the 2025 Winter Simulation Conference, pp. 378–389, 2025.
S. Moro, P. Rita, P. Cortez. Bank Marketing. UCI Machine Learning Repository, 2014. Dataset. https://doi.org/10.24432/C5K306.
White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817–838. https://doi.org/10.2307/1912934.
Cameron, A. Colin, and Douglas L. Miller. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources, vol. 50, no. 2, 2015, pp. 317–372. https://doi.org/10.3368/jhr.50.2.317.
Rasmussen, Carl Edward, and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. https://gaussianprocess.org/gpml/.
Ankenman, Bruce, Barry L. Nelson, and Jeremy Staum. “Stochastic Kriging for Simulation Metamodeling.” Operations Research, vol. 58, no. 2, 2010, pp. 371–382. https://doi.org/10.1287/opre.1090.0754.
Kim, Seong-Hee, and Barry L. Nelson. “A Fully Sequential Procedure for Indifference-Zone Selection in Simulation.” ACM Transactions on Modeling and Computer Simulation, vol. 11, no. 3, 2001, pp. 251–273. https://doi.org/10.1145/502109.502111.
Frazier, Peter I., Warren B. Powell, and Savas Dayanik. “A Knowledge-Gradient Policy for Sequential Information Collection.” SIAM Journal on Control and Optimization, vol. 47, no. 5, 2008, pp. 2410–2439. https://doi.org/10.1137/070693424.
Bertsimas, Dimitris, and Melvyn Sim. “The Price of Robustness.” Operations Research, vol. 52, no. 1, 2004, pp. 35–53. https://doi.org/10.1287/opre.1030.0065.