Asymptotic normality of a generalized maximum mean discrepancy estimator

Statistics & Probability Letters ◽

10.1016/j.spl.2020.108961 ◽

2021 ◽

Vol 169 ◽

pp. 108961

Author(s):

Armando Sosthène Kali Balogoun ◽

Guy Martial Nkiet ◽

Carlos Ogouyandjou

Keyword(s):

Asymptotic Normality ◽

Maximum Mean Discrepancy

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Asymptotic normality of a consistent estimator of maximum mean discrepancy in Hilbert space

Statistics & Probability Letters ◽

10.1016/j.spl.2019.108596 ◽

2020 ◽

Vol 156 ◽

pp. 108596 ◽

Author(s):

Natsumi Makigusa ◽

Kanta Naito

Keyword(s):

Hilbert Space ◽

Asymptotic Normality ◽

Consistent Estimator ◽

Maximum Mean Discrepancy

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Asymptotic normality of processes with a branching structure

Communication in Statistics- Theory and Methods ◽

10.1080/03610929808828951 ◽

1998 ◽

Vol 14 (4) ◽

pp. 833-848

Author(s):

Malcolm P. Quine ◽

Władysław Szczotka

Keyword(s):

Asymptotic Normality

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Operational risk models and asymptotic normality of maximum likelihood estimation

The Journal of Operational Risk ◽

10.21314/jop.2016.183 ◽

2016 ◽

Vol 11 (4) ◽

pp. 55-78 ◽

Author(s):

Paul Larsen

Keyword(s):

Maximum Likelihood ◽

Maximum Likelihood Estimation ◽

Asymptotic Normality ◽

Operational Risk ◽

Likelihood Estimation ◽

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Asymptotic normality for discretely observed Markov jump processes with an absorbing state

Statistics & Probability Letters ◽

10.1016/j.spl.2014.03.010 ◽

2014 ◽

Vol 90 ◽

pp. 136-139 ◽

Author(s):

Alexander Kremer ◽

Rafael Weißbach

Keyword(s):

Asymptotic Normality ◽

Jump Processes ◽

Markov Jump ◽

Markov Jump Processes ◽

Absorbing State

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Asymptotic Normality of an Estimator of an Infinite-dimensional Parameter in a Model with a C1smooth Regression Function

Exploring Stochastic Laws ◽

10.1515/9783112318768-026 ◽

1995 ◽

pp. 251-256

Keyword(s):

Asymptotic Normality ◽

Regression Function ◽

Dimensional Parameter ◽

Infinite Dimensional

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Incorporate Maximum Mean Discrepancy in Recurrent Latent Space for Sequential Generative Model

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp39728.2021.9414580 ◽

2021 ◽

Author(s):

Yuchi Zhang ◽

Yongliang Wang ◽

Yang Dong

Keyword(s):

Generative Model ◽

Maximum Mean Discrepancy ◽

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Asymptotic Normality of Experiments

Encyclopedia of Statistical Sciences ◽

10.1002/0471667196.ess0595.pub2 ◽

2006 ◽

Author(s):

L. Le Cam

Keyword(s):

Asymptotic Normality

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EDA on the asymptotic normality of the standardized sequential stopping times, Part-II: Distribution-free models

Sequential Analysis ◽

10.1080/07474946.2020.1823193 ◽

2020 ◽

Vol 39 (3) ◽

pp. 367-398

Author(s):

Nitis Mukhopadhyay ◽

Chen Zhang

Keyword(s):

Asymptotic Normality ◽

Stopping Times ◽

Distribution Free

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Bayesian Neural Networks with Maximum Mean Discrepancy regularization

Neurocomputing ◽

10.1016/j.neucom.2021.01.090 ◽

2021 ◽

Author(s):

Jary Pomponi ◽

Simone Scardapane ◽

Aurelio Uncini

Keyword(s):

Neural Networks ◽

Maximum Mean Discrepancy ◽

Bayesian Neural Networks

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A model for random instantaneous growth on an interval

Journal of Applied Probability ◽

10.2307/3214489 ◽

1991 ◽

Vol 28 (3) ◽

pp. 529-538

Author(s):

M. P. Quine

Keyword(s):

Asymptotic Normality ◽

Instantaneous Growth ◽

Mean And Variance ◽

Points arrive in succession on an interval and immediately ‘cover' a region of length ½ to each side (less if they are close to the boundary or to a covered part). The location of a new point is uniformly distributed on the uncovered parts. We study the mean and variance of the total number of points ever formed, in particular as a → 0, in which case we also establish asymptotic normality.

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