Davy Paindaveine

Davy Paindaveine, J. Remy, and Thomas Verdebout, “Testing for principal component directions under weak identifiability”, Annals of Statistics, vol. 48, n. 1, 2020, pp. 324–345.

Davy Paindaveine, and Thomas Vertdebout, “Inference for spherical location under high concentration”, Annals of Statistics, vol. 48, 2020, pp. 2982–2998.

Davy Paindaveine, and Thomas Verdebout, “Detecting the direction of a signal on high-dimensional spheres: non-null and Le Cam optimality results”, Probability Theory and Related Fields, vol. 176, 2020, pp. 1165–1216.

Eduardo Garcia-Portugues, Davy Paindaveine, and Thomas Verdebout, “On optimal tests for rotational symmetry against new classes of hyperspherical distributions”, Journal of the American Statistical Association, vol. 115, 2020, pp. 1873–1887.

Davy Paindaveine, and G. Van Bever, “Tyler shape depth”, Biometrika, vol. 106, n. 4, December 2019, pp. 913–927.

Davy Paindaveine, G. Pandolfo, and G. Porzio, “Distance-based depths for directional data”, Canadian Journal of Statistics, n. 46, 2018, pp. 593–609.

Davy Paindaveine, and G. Van Bever, “Halfspace depths for scatter, concentration and shape matrices”, Annals of Statistics, vol. 46, 2018, pp. 3276–3307.

Christine Cutting, Davy Paindaveine, and Thomas Verdebout, “Testing uniformity on high-dimensional spheres against monotone rotationally symmetric alternatives”, Annals of Statistics, vol. 45, 2017, pp. 1024–1058.

Davy Paindaveine, and Thomas Verdebout, “Inference on the mode of weak directional signals: a Le Cam perspective on hypothesis testing near singularities”, Annals of Statistics, vol. 45, 2017, pp. 800–832.