Preprints and Unpublished Manuscripts
Preprints
Chang, W., Kuchibhotla A. K. and Rinaldo, A. (2023). Inference for Projection Parameters in Linear Regression: beyond d=o(n^{1/2}) arXiv
Wang, F., Li, W., Padilla, O. H., Yu, Y. and Rinaldo, A. (2023) Multilayer random dot product graphs: Estimation and online change point detection. arXiv
Bong, H., Kuchibhotla A. K. and Rinaldo, A. (2023). Dual Induction CLT for High-dimensional m-dependent Data. arXiv
Li, G., Wu, W., Chi, Y., Ma, C., Rinaldo, A. and Wei, Y. (2023). Sharp high-probability sample complexities for policy evaluation with linear function approximation. arXiv
Li, W., Wang, D. and Rinaldo, A. (2023). Divide and Conquer Dynamic Programming: An Almost Linear Time Change Point Detection Methodology in High Dimensions. arXiv
Gangrade, A., Rinaldo, A. and Ramdas, A. (2023). A Sequential Test for Log-Concavity arXiv
Bong, H., Kuchibhotla, A. K. and Rinaldo, A. (2023). High-dimensional Berry-Esseen Bound for m-Dependent Random Samples arXiv
Patil, P., Kuchibhotla, A. K., Wei, Y. and Rinaldo, A. (2022). Mitigating multiple descents: A model-agnostic framework for risk monotonization. arXiv
Shin, J., Ramdas, A. and Rinaldo, A. (2022). E-detectors: a nonparametric framework for online changepoint detection. arXiv
Yi, Y., Madrid Padilla, O.H., Wang, D. and Rinaldo, A. (2021). Optimal network online change point localisation. arXiv
Kuchibhotla, A.K., Rinaldo, A. and Wasserman, L. (2020). Berry-Esseen Bounds for Projection Parameters and Partial Correlations with Increasing Dimension arXiv
Madrid Padilla, O.H., Yu, Y., Wang, D. And Rinaldo, A. (2020). A Note on Online Change Point Detection. arXiv
Zhao, D., Rinaldo, A. and Brookins, C. (2019), Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines. arXiv
Wang, D., Yu., Y., Rinaldo, A. and Willett, R. (2019). Localizing Changes in High-Dimensional Vector Autoregressive Processes. arXiv R code
Shin, J., Rinaldo, A. And Wasserman, L. (2019). Predictive Clustering. arXiv
Kim, K., Kim, J. and Rinaldo, A. (2018). Time Series Featurization via Topological Data Analysis: an Application to Cryptocurrency Trend Forecasting. arXiv
Kim., J., Shin, J., Rinaldo, A. and Wasserman, L. (2017). Confidence sets for persistent homology of the KDE filtration. pdf
Unpublished Monograph
Rinaldo, A. (2006). Computing Maximum Likelihood Estimates in Log-Linear Models. Manuscript proposing computational procedures for extended maximum likelihood estimation (extracted from my PhD thesis). draft pdf. A small MATLAB toolbox implementing some of the routines described in the document, which was written for testing purposes, is also available here (without any warranty!).
Unpublished Manuscripts
Chen,Y.C., Wang, D., Rinaldo, A. and Wasserman, L. (2015). Statistical Analysis of Persistence Intensity Functions. arXiv
Kent, B. P., Rinaldo, A. and Verstynen, T. (2013). DeBaCl: A Python Package for Interactive DEnsity-BAsed CLustering. arXiv Download DeBaCl
Balakrishnan, S., Narayanan, S., Rinaldo, A., Singh, A. and Wasserman, L. (2013). Tight Lower Bounds for Homology Inference. arXiv
Rinaldo, A., Petrovíc, S. and Fienberg, S.E. (2010). On the Existence of the MLE for a Directed Random Graph Network Model with Reciprocation. arXiv
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