Preprints and Unpublished Manuscripts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. and Rinaldo, A. (2020). High-dimensional CLT for Sums of Non-degenerate Random Vectors: n−1/2-rate.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Unpublished Monograph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

• Kim, N. and Rinaldo, A. (2017). Community Detection on Ego Networks via Mutual Friend Counts. pdf

• Kim, N. and Rinaldo, A. (2017). Edge-Induced Sampling from Graphons. pdf

• 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