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Given a quantum observable and a state, one can construct a classical observable, such that it is the optimal estimate of the quantum observable, in the sense of minimum variance. We demonstrate how t...
Parameter estimation or filtering is one of the important issues in diverse fields including statistical learning, signal processing, system identification and adaptive control. With the development o...
We develop the Double Principal Component Analysis (DPCA) based on a dual factor structure for high-frequency intraday returns contaminated by microstructure noise. The dual factor structure allows a ...
This talk considers the distributed estimation problem of a linear stochastic system described by an autoregressive model with exogenous inputs when both the system orders and parameters are unknown. ...
We present recent developments in double machine learning (DML) approach. The DML approach is concerned primarily with selecting the relevant control variables and functional forms necessary for the c...
The rank-tracking probability (RTP) is a useful statistical index for measuring the ``tracking ability'' of longitudinal disease risk factors in biomedical studies. A flexible nonparametric method for...
Background: Rapid and accurate plant growth and biomass estimation is essential for formulating and implementing targeted forest cultivation measures. In this study, RGB-D imaging technology was used ...

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