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Propensity score analysis using the freely available user-friendly software EZR (Easy R). [PDF]

Blood Res
Kanda Y.
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Proactive fault prediction in marine diesel engines using multivariate machine learning. [PDF]

Sci Rep
Michel M, Mehanna A, Saleh SN, Shehata AS.   +3 more
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Machine learning-based temperature prediction across diverse ecosystems for the Boro Season in Bangladesh. [PDF]

Sci Rep
Rahman NMF, Haque N, Asadullah M, Hossain ABMZ, Hossain MS, Uddin MJ, Hassan SMQ, Baten MA.   +7 more
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Identification of multivariable errors-in-variables models

1999 European Control Conference (ECC), 1999
The paper deals with a new identification approach, based on a prediction error method, for multivariable errors-in-variables models (EIV). Starting from the ARMAX decomposition of MIMO EIV processes and congruence conditions between noisy sequences and the constraints of EIV representations, the simultaneous estimate of the model parameters and of the
Paolo Castaldi, Roberto Diversi, Roberto Guidorzi, Umberto Soverini   +3 more
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Identification of multivariable errors in variable models with dynamics

IEEE Transactions on Automatic Control, 1986
This paper extends to the multivariable case the results presented for scalar systems by the second author [Automatica 21, 709-716 (1985)]. The problem considered is that of identifying a causal, linear, dynamic multivariable system from measurements of the input and output signals corrupted by noises of unknown spectra. To solve this so-called errors-
Green, Michael, Anderson, Brian D. O.
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Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Communications in Statistics - Simulation and Computation, 2009
Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated,
Cibele M. Russo, Reiko Aoki, Dorival Leao Pinto Junior   +2 more
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Frisch scheme–based identification of multivariable errors–in–variables models

IFAC Proceedings Volumes, 2009
Abstract This paper describes an identification procedure for minimally parametrized multivariable models in the Errors–in–Variables (EIV) context of the Frisch scheme that considers additive white observation noise on the process inputs and outputs.
DIVERSI, ROBERTO, GUIDORZI, ROBERTO
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Subspace algorithms for the identification of multivariable dynamic errors-in-variables models

Autom., 1997
This paper deals with the problem of identifying multivariable finite dimensional linear time-invariant systems from noisy input/output measurements. A solution is obtained by means of subspace identification algorithms. Some SMI algorithms that consistently estimate state space models are presented.
Chun Tung Chou, Michel Verhaegen
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