The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform
Natasha Devroye - One of the best experts on this subject based on the ideXlab platform.
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Zero-Error Relaying for Primitive Relay Channels
IEEE Transactions on Information Theory, 2017Co-Authors: Yanying Chen, Natasha DevroyeAbstract:In a primitive relay channel, a new one-shot relaying scheme termed color-and-forward is proposed that guarantees a probability of error equal to zero. This relaying scheme constructs a relaying compression graph of relay outputs based on the Joint Conditional Distribution of the relay and destination outputs, and forwards a minimum coloring of this graph. The $n$ -letter extension of the proposed color-and-forward scheme is shown to be optimal in the sense that it results in the smallest needed out-of-band relay to destination link rate for the overall message rate to equal the single-input multiple-output outer bound for any fixed number of channel uses. This is used to obtain an upper bound on the asymptotic minimal relay to destination link rate needed to achieve the single-input multiple-output outer bound.
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Allerton - Colour-and-Forward: Relaying “what the destination needs” in the zero-error primitive relay channel
2014 52nd Annual Allerton Conference on Communication Control and Computing (Allerton), 2014Co-Authors: Yanying Chen, Sara Shahi, Natasha DevroyeAbstract:Zero-error communication over a primitive relay channel is for the first time proposed and studied. This model is used to highlight how one may exploit the channel structure to design a relaying strategy that explicitly provides “what destination needs”. We propose the Colour-and-Forward relaying scheme which constructs a graph G R of relay outputs based on the Joint Conditional Distribution of the relay and destination outputs given the channel input. The colours of this graph G R are sent over the out-of-band link in the primitive relay channel and are shown to be information lossless in the zero-error sense; they result in the same confusability graph as if the destination had the relay's received signal. This allows us to obtain an achievable zero-error communication rate for the primitive relay channel, which may be shown to be capacity for a class of channels.
Yanying Chen - One of the best experts on this subject based on the ideXlab platform.
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Zero-Error Relaying for Primitive Relay Channels
IEEE Transactions on Information Theory, 2017Co-Authors: Yanying Chen, Natasha DevroyeAbstract:In a primitive relay channel, a new one-shot relaying scheme termed color-and-forward is proposed that guarantees a probability of error equal to zero. This relaying scheme constructs a relaying compression graph of relay outputs based on the Joint Conditional Distribution of the relay and destination outputs, and forwards a minimum coloring of this graph. The $n$ -letter extension of the proposed color-and-forward scheme is shown to be optimal in the sense that it results in the smallest needed out-of-band relay to destination link rate for the overall message rate to equal the single-input multiple-output outer bound for any fixed number of channel uses. This is used to obtain an upper bound on the asymptotic minimal relay to destination link rate needed to achieve the single-input multiple-output outer bound.
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Allerton - Colour-and-Forward: Relaying “what the destination needs” in the zero-error primitive relay channel
2014 52nd Annual Allerton Conference on Communication Control and Computing (Allerton), 2014Co-Authors: Yanying Chen, Sara Shahi, Natasha DevroyeAbstract:Zero-error communication over a primitive relay channel is for the first time proposed and studied. This model is used to highlight how one may exploit the channel structure to design a relaying strategy that explicitly provides “what destination needs”. We propose the Colour-and-Forward relaying scheme which constructs a graph G R of relay outputs based on the Joint Conditional Distribution of the relay and destination outputs given the channel input. The colours of this graph G R are sent over the out-of-band link in the primitive relay channel and are shown to be information lossless in the zero-error sense; they result in the same confusability graph as if the destination had the relay's received signal. This allows us to obtain an achievable zero-error communication rate for the primitive relay channel, which may be shown to be capacity for a class of channels.
Michel Gendreau - One of the best experts on this subject based on the ideXlab platform.
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A stochastic program with time series and affine decision rules for the reservoir management problem
European Journal of Operational Research, 2018Co-Authors: Charles Gauvin, Erick Delage, Michel GendreauAbstract:Abstract This paper proposes a multi-stage stochastic programming formulation for the reservoir management problem. Our problem specifically consists in minimizing the risk of floods over a fixed time horizon for a multi-reservoir hydro-electrical complex. We consider well-studied linear time series models and enhance the approach to consider heteroscedasticity. Using these stochastic processes under very general Distributional assumptions, we efficiently model the support of the Joint Conditional Distribution of the random inflows and update these sets as new data are assimilated. Using robust optimization techniques and affine decision rules, we embed these time series in a tractable convex program. This allows us to obtain good quality solutions rapidly and test our model in a realistic simulation framework using a rolling horizon approach. Finally, we study a river system in western Quebec and perform various numerical experiments based on different inflow generators.
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A stochastic program with time series and affine decision rules for the reservoir management problem
2016Co-Authors: Charles Gauvin, Erick Delage, Michel GendreauAbstract:This paper proposes a multi-stage stochastic programming formulation for the reservoir management problem. Our problem specifically consists in minimizing the risk of floods over a fixed time horizon for a multi-dimensional hydro-electrical complex. We consider well-studied linear time series model and enhance the approach to consider heteroscedasticity. Using these stochastic processes under very general Distributional assumptions, we efficiently model the support of the Joint Conditional Distribution of the random inflows and update these sets as new data is assimilated. Using robust optimization techniques and affine decision rules, we embed these time series in a tractable convex program. This allows us to obtain good quality solutions rapidly and test our model in a realistic simulation framework using a rolling horizon approach. Finally, we study a real river system in Western Quebec and perform various numerical experiments based on different inflow generators.
Yongmiao Hong - One of the best experts on this subject based on the ideXlab platform.
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A Unified Approach to Validating Univariate and Multivariate Conditional Distribution Models in Time Series
Journal of Econometrics, 2014Co-Authors: Bin Chen, Yongmiao HongAbstract:Abstract Modeling Conditional Distributions in time series has attracted increasing attention in economics and finance. We develop a new class of generalized Cramer–von Mises (GCM) specification tests for time series Conditional Distribution models using a novel approach, which embeds the empirical Distribution function in a spectral framework. Our tests check a large number of lags and are therefore expected to be powerful against neglected dynamics at higher order lags, which is particularly useful for non-Markovian processes. Despite using a large number of lags, our tests do not suffer much from loss of a large number of degrees of freedom, because our approach naturally downweights higher order lags, which is consistent with the stylized fact that economic or financial markets are more affected by recent past events than by remote past events. Unlike the existing methods in the literature, the proposed GCM tests cover both univariate and multivariate Conditional Distribution models in a unified framework. They exploit the information in the Joint Conditional Distribution of underlying economic processes. Moreover, a class of easy-to-interpret diagnostic procedures are supplemented to gauge possible sources of model misspecifications. Distinct from conventional CM and Kolmogorov–Smirnov (KS) tests, which are also based on the empirical Distribution function, our GCM test statistics follow a convenient asymptotic N ( 0 , 1 ) Distribution and enjoy the appealing “nuisance parameter free” property that parameter estimation uncertainty has no impact on the asymptotic Distribution of the test statistics. Simulation studies show that the tests provide reliable inference for sample sizes often encountered in economics and finance.
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Characteristic Function-Based Testing for Multifactor Continuous-Time Markov Models via Nonparametric Regression
2013Co-Authors: Bin Chen, Yongmiao HongAbstract:We develop a nonparametric regression-based goodness-of-fit test for multifactor continuous-time Markov models using the Conditional characteristic function, which often has a convenient closed form or can be approximated accurately for many popular continuous-time Markov models in economics and finance. An omnibus test fully utilizes the information in the Joint Conditional Distribution of the underlying processes and hence has power against a vast class of continuous-time alternatives in the multifactor framework. A class of easy-to-interpret diagnostic procedures is also proposed to gauge possible sources of model misspecification. All the proposed test statistics have a convenient asymptotic N(0,1) Distribution under correct model specification, and all asymptotic results allow for some data-dependent bandwidth. Simulations show that in finite samples, our tests have reasonable size, thanks to the dimension reduction in nonparametric regression, and good power against a variety of alternatives, including misspecifications in the Joint dynamics, but the dynamics of each individual component is correctly specified. This feature is not attainable by some existing tests. A parametric bootstrap improves the finite-sample performance of proposed tests but with a higher computational cost.
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Generalized spectral testing for multivariate continuous-time models
Journal of Econometrics, 2011Co-Authors: Bin Chen, Yongmiao HongAbstract:We develop an omnibus specification test for multivariate continuous-time models using the Conditional characteristic function, which often has a convenient closed-form or can be accurately approximated for many multivariate continuous-time models in finance and economics. The proposed test fully exploits the information in the Joint Conditional Distribution of underlying economic processes and hence is expected to have good power in a multivariate context. A class of easy-to-interpret diagnostic procedures is supplemented to gauge possible sources of model misspecification. Our tests are also applicable to discrete-time Distribution models. Simulation studies show that the tests provide reliable inference in finite samples.
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CHARACTERISTIC FUNCTION–BASED TESTING FOR MULTIFACTOR CONTINUOUS-TIME MARKOV MODELS VIA NONPARAMETRIC REGRESSION
Econometric Theory, 2009Co-Authors: Bin Chen, Yongmiao HongAbstract:We develop a nonparametric regression-based goodness-of-fit test for multifactor continuous-time Markov models using the Conditional characteristic function, which often has a convenient closed form or can be approximated accurately for many popular continuous-time Markov models in economics and finance. An omnibus test fully utilizes the information in the Joint Conditional Distribution of the underlying processes and hence has power against a vast class of continuous-time alternatives in the multifactor framework. A class of easy-to-interpret diagnostic procedures is also proposed to gauge possible sources of model misspecification. All the proposed test statistics have a convenient asymptotic N (0, 1) Distribution under correct model specification, and all asymptotic results allow for some data-dependent bandwidth. Simulations show that in finite samples, our tests have reasonable size, thanks to the dimension reduction in nonparametric regression, and good power against a variety of alternatives, including misspecifications in the Joint dynamics, but the dynamics of each individual component is correctly specified. This feature is not attainable by some existing tests. A parametric bootstrap improves the finite-sample performance of proposed tests but with a higher computational cost.
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Characteristic Function-Based Testing for Multifactor Continuous-Time Markov Models Via Nonparametric Regression
2008Co-Authors: Bin Chen, Yongmiao HongAbstract:We develop a nonparametric regression-based goodness-of-fit test for multifactor continuous-time Markov models using the Conditional characteristic function, which often has a convenient closed-form or can be approximated accurately for many popular continuous-time Markov models in economics and finance. An omnibus test procedure fully utilizes the information in the Joint Conditional Distribution of the underlying processes and hence has power against a vast class of continuous-time alternatives in the multifactor framework. A class of easy-to-interpret diagnostic procedures is also proposed to gauge possible sources of model misspecifications. All our test statistics have a convenient asymptotic N(0,1) Distribution under correct model specification. Simulations show that our tests have reasonable size, thanks to the dimension reduction in nonparametric regression, and good power against a variety of alternatives, including misspecifications in the Joint dynamics even if the dynamics of each individual component is correctly specified. This feature is not attainable by some existing tests. A parametric bootstrap improves the finite sample performance of proposed tests, but with higher computational costs.
Sara Shahi - One of the best experts on this subject based on the ideXlab platform.
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Allerton - Colour-and-Forward: Relaying “what the destination needs” in the zero-error primitive relay channel
2014 52nd Annual Allerton Conference on Communication Control and Computing (Allerton), 2014Co-Authors: Yanying Chen, Sara Shahi, Natasha DevroyeAbstract:Zero-error communication over a primitive relay channel is for the first time proposed and studied. This model is used to highlight how one may exploit the channel structure to design a relaying strategy that explicitly provides “what destination needs”. We propose the Colour-and-Forward relaying scheme which constructs a graph G R of relay outputs based on the Joint Conditional Distribution of the relay and destination outputs given the channel input. The colours of this graph G R are sent over the out-of-band link in the primitive relay channel and are shown to be information lossless in the zero-error sense; they result in the same confusability graph as if the destination had the relay's received signal. This allows us to obtain an achievable zero-error communication rate for the primitive relay channel, which may be shown to be capacity for a class of channels.
