2025 Projects.

We introduce the Cradle prior, a new global–local shrinkage prior that combines a half-Laplace local scale with a half-Cauchy global scale to tightly shrink small coefficients while accommodating moderately large ones. Empirical and theoretical results suggest that Cradle often outperforms existing methods (e.g., horseshoe, Bayesian Lasso), particularly in sparse settings with moderately large effect sizes, such as genomic applications.

We analyze daily lead-time distributions for two Airbnb demand metrics, Nights Booked (volume-based) and Gross Booking Value (revenue-based), using compositional data transformations and extensive modeling techniques. They find that revenue-based demand systematically diverges from volume-based demand in a mid-range horizon, with a parsimonious Gamma distribution providing consistently strong day-level fits for both metrics, even outperforming spline-based methods in terms of divergence measures.

We present the B-DARMA model for analyzing compositional time series, exemplified using daily S&P 500 sector trading value proportions. They provide a step-by-step tutorial in Stan, covering model specification, handling simplex constraints, incorporating ARMA and time-varying volatility terms, and performing posterior predictive checks and forecasting—methods that extend to various fields with simplex-valued, temporally dependent data.

The rise of remote work has sparked widespread claims that stays in short-term rentals are getting longer, but rigorous, nation-wide evidence remains limited. This study interrogates that claim by assembling a booking-weighted panel of U.S.\ Airbnb reservations spanning 2019–2024 and by applying a comprehensive suite of statistical tools. We fit Gamma and log-normal distributions via weighted maximum likelihood to characterise the full shape of the nights-per-booking (NPB) distribution; we quantify pandemic-phase shifts with weighted negative-binomial regression and a two-part hurdle model that isolates long-stay behaviour; and we capture month-to-month dynamics with a seasonal ARIMA(0,1,1)(0,1,1)12\mathrm{ARIMA}(0,1,1)(0,1,1)_{12}ARIMA(0,1,1)(0,1,1)12​ specification. Together, these approaches allow us to (i) benchmark competing density forms, (ii) separate changes in the frequency versus the duration of long stays, and (iii) assess the forecasting value of pandemic-era regime indicators. The paper offers the first large-scale, post-pandemic portrait of U.S.\ Airbnb length-of-stay patterns and sets the stage for discussing how remote-work–driven travel may reconfigure pricing strategy, zoning policy, and lodging-tax design.