Convergence rate of Krasulina estimator

A fine-tuned estimator of a general convergence rate

A general rate estimation method based on the in-sample evolution of appropriately chosen diverging/converging statistics has been proposed in Politis (2002) and McElroy and Politis (2007). In this paper, we show how a modification of the original estimators achieves a competitive rate of convergence. The modified estimators require the choice of a tuning parameter; an optimal such choice is ge...

Convergence rate of Bayesian tensor estimator and its minimax optimality

We investigate the statistical convergence rate of a Bayesian low-rank tensor estimator, and derive the minimax optimal rate for learning a lowrank tensor. Our problem setting is the regression problem where the regression coefficient forms a tensor structure. This problem setting occurs in many practical applications, such as collaborative filtering, multi-task learning, and spatiotemporal dat...

Convergence Rate of the Causal Jacobi Derivative Estimator

Numerical causal derivative estimators from noisy data are essential for real time applications especially for control applications or fluid simulation so as to address the new paradigms in solid modeling and video compression. By using an analytical point of view due to Lanczos [9] to this causal case, we revisit n order derivative estimators originally introduced within an algebraic framework...

Convergence rate of Bayesian tensor estimator and its minimax optimality

Convergence rate of Bayesian tensor estimator: Optimal rate without restricted strong convexity

In this paper, we investigate the statistical convergence rate of a Bayesian low-rank tensor estimator. Our problem setting is the regression problem where a tensor structure underlying the data is estimated. This problem setting occurs in many practical applications, such as collaborative filtering, multi-task learning, and spatio-temporal data analysis. The convergence rate is analyzed in ter...