@article{dixit_martin_2023, title={A PRticle filter algorithm for nonparametric estimation of multivariate mixing distributions}, volume={33}, ISSN={["1573-1375"]}, DOI={10.1007/s11222-023-10242-2}, abstractNote={Predictive recursion (PR) is a fast, recursive algorithm that gives a smooth estimate of the mixing distribution under the general mixture model. However, the PR algorithm requires evaluation of a normalizing constant at each iteration. When the support of the mixing distribution is of relatively low dimension, this is not a problem since quadrature methods can be used and are very efficient. But when the support is of higher dimension, quadrature methods are inefficient and there is no obvious Monte Carlo-based alternative. In this paper, we propose a new strategy, which we refer to as PRticle filter, wherein we augment the basic PR algorithm with a filtering mechanism that adaptively reweights an initial set of particles along the updating sequence which are used to obtain Monte Carlo approximations of the normalizing constants. Convergence properties of the PRticle filter approximation are established and its empirical accuracy is demonstrated with simulation studies and a marked spatial point process data analysis.}, number={4}, journal={STATISTICS AND COMPUTING}, author={Dixit, Vaidehi and Martin, Ryan}, year={2023}, month={Aug} }
 @article{dixit_martin_2023, title={Revisiting consistency of a recursive estimator of mixing distributions}, volume={17}, ISSN={["1935-7524"]}, DOI={10.1214/23-EJS2121}, abstractNote={Estimation of the mixing distribution under a general mixture model is a very difficult problem, especially when the mixing distribution is assumed to have a density. Predictive recursion (PR) is a fast, recursive algorithm for nonparametric estimation of a mixing distribution/density in general mixture models. However, the existing PR consistency results make rather strong assumptions, some of which fail for a class of mixture models relevant for monotone density estimation, namely, scale mixtures of uniform kernels. In this paper, we develop new consistency results for PR under weaker conditions. Armed with this new theory, we prove that PR is consistent for the scale mixture of uniforms problem, and we show that the corresponding PR mixture density estimator has very good practical performance compared to several existing methods for monotone density estimation.}, number={1}, journal={ELECTRONIC JOURNAL OF STATISTICS}, author={Dixit, Vaidehi and Martin, Ryan}, year={2023}, pages={1007–1042} }
 @article{dixit_martin_2022, title={Estimating a Mixing Distribution on the Sphere Using Predictive Recursion}, ISSN={["0976-8394"]}, DOI={10.1007/s13571-021-00275-w}, abstractNote={Mixture models are commonly used when data show signs of heterogeneity and, often, it is important to estimate the distribution of the latent variable responsible for that heterogeneity. This is a common problem for data taking values in a Euclidean space, but the work on mixing distribution estimation based on directional data taking values on the unit sphere is limited. In this paper, we propose using the predictive recursion (PR) algorithm to solve for a mixture on a sphere. One key feature of PR is its computational efficiency. Moreover, compared to likelihood-based methods that only support finite mixing distribution estimates, PR is able to estimate a smooth mixing density. PR’s asymptotic consistency in spherical mixture models is established, and simulation results showcase its benefits compared to existing likelihood-based methods. Using PR we propose a method for goodness-of-fit testing and a clustering mechanism in the context of directional data with two real-data illustrations.}, journal={SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS}, author={Dixit, Vaidehi and Martin, Ryan}, year={2022}, month={Feb} }
 @article{perdew_emke_johnson_dixit_song_griffith_watson_gruen_2021, title={Evaluation of Pexion(R) (imepitoin) for treatment of storm anxiety in dogs: A randomised, double-blind, placebo-controlled trial}, volume={188}, ISSN={["2042-7670"]}, url={https://doi.org/10.1002/vetr.18}, DOI={10.1002/vetr.18}, abstractNote={AbstractBackground: While often grouped with other noise aversions, fearful behaviour during storms is considered more complex than noise aversion alone. The objective here was to assess the effect of imepitoin for the treatment of storm anxiety in dogs.Methods: In this double‐blind, placebo‐controlled randomised study, eligible dogs completed a baseline then were randomised to receive either imepitoin (n = 30; 30 mg/kg BID) or placebo (n = 15) for 28 days. During storms, owners rated their dog's intensity for 16 behaviours using a Likert scale. Weekly, owners rated intensity and frequency of these behaviours. Summary scores were compared to baseline and between groups.Results and Conclusions: Imepitoin was significantly superior to placebo in storm logs and weekly surveys for weeks 2 and 4, and in the end‐of‐study survey. Mild/moderate adverse events were reported in 26 patients (24 active: two placebo); the most frequent adverse event was ataxia. Owners of dogs in the imepitoin group, compared to placebo, were significantly more likely to report that treatment reduced their dogs fear and anxiety during storms (p < 0.001) and other noise events (p < 0.001). Twice daily administration of imepitoin decreased anxiety scores in dogs with storm anxiety. Future work may evaluate optimal dosage regimens.}, number={9}, journal={VETERINARY RECORD}, publisher={Wiley}, author={Perdew, Irina and Emke, Carrie and Johnson, Brianna and Dixit, Vaidehi and Song, Yukun and Griffith, Emily H. and Watson, Philip and Gruen, Margaret E.}, year={2021}, month={May} }
 @article{dixit_mitra_simonsen_2021, title={Multi-arm multi-stage clinical trials for time-to-event outcomes}, ISSN={["1520-5711"]}, DOI={10.1080/10543406.2021.1979575}, abstractNote={ABSTRACT This paper investigates the use of a general multi-arm multi-stage (MAMS) approach for time-to-event outcomes that would streamline simultaneous comparison of a large number of promising therapies in clinical trials, thus significantly reducing the time and the number of patients needed to evaluate the treatment. Controlling type I error in this setting is different than regular clinical trials as this approach incorporates both multiple comparison between arms and multiple stages. Historically, pairwise (PWER) and familywise (FWER) type I error rates have been primarily used to regulate the type I error in such designs. This paper will focus on constructing the efficacy and futility boundaries for a MAMS clinical trial in two different scenarios. In the first, it is assumed that the same outcome is used throughout the clinical trial for both intermediate and final assessments. In this scenario, we propose using the generalized Dunnett procedure that controls FWER. In the latter scenario, where intermediate and final outcomes are different in nature, we propose modifications to the existing method that originally concentrated on controlling PWER and extend the method to include FWER in the design. We also explore the performance of the proposed MAMS design in a setting where the proportional hazard assumption is violated in the presence of a delayed treatment effect and demonstrate the loss of power because of that. An alternative test statistic that can help circumvent this problem to maintain the desired power is also suggested.}, journal={JOURNAL OF BIOPHARMACEUTICAL STATISTICS}, author={Dixit, Vaidehi and Mitra, Priyam and Simonsen, Katy}, year={2021}, month={Oct} }