Online Updating of Statistical Inference in the Big Data Setting

We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residual tests that can be used to assess the goodness-of-fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting.Comment: Submitted to Technometric

Non-perturbative Dynamical Decoupling Control: A Spin Chain Model

This paper considers a spin chain model by numerically solving the exact model to explore the non-perturbative dynamical decoupling regime, where an important issue arises recently (J. Jing, L.-A. Wu, J. Q. You and T. Yu, arXiv:1202.5056.). Our study has revealed a few universal features of non-perturbative dynamical control irrespective of the types of environments and system-environment couplings. We have shown that, for the spin chain model, there is a threshold and a large pulse parameter region where the effective dynamical control can be implemented, in contrast to the perturbative decoupling schemes where the permissible parameters are represented by a point or converge to a very small subset in the large parameter region admitted by our non-perturbative approach. An important implication of the non-perturbative approach is its flexibility in implementing the dynamical control scheme in a experimental setup. Our findings have exhibited several interesting features of the non-perturbative regimes such as the chain-size independence, pulse strength upper-bound, noncontinuous valid parameter regions, etc. Furthermore, we find that our non-perturbative scheme is robust against randomness in model fabrication and time-dependent random noise

Parameterized Multi-observable Sum Uncertainty Relations

The uncertainty principle is one of the fundamental features of quantum mechanics and plays an essential role in quantum information theory. We study uncertainty relations based on variance for arbitrary finite $N$ quantum observables. We establish a series of parameterized uncertainty relations in terms of the parameterized norm inequalities, which improve the exiting variance-based uncertainty relations. The lower bounds of our uncertainty inequalities are non-zero unless the measured state is the common eigenvector of all the observables. Detailed examples are provided to illustrate the tightness of our uncertainty relations.Comment: 12 pages, 3 figure