Statistical Inference Under Multiterminal Data Compression

By Te Sun Han, Shun-ichi Amari. 1998

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Citations in the corpus

Inference Under Information Constraints I: Lower Bounds From Chi-Square Contraction (2018, background): “In a parallel thread, distributed statistical inference under communication constraints was initially studied in the information theory community [5], [30], [31], with the objective to characterize the asymptotic error exponents as a function of the communication rate.”
Information-Theoretic Lower Bounds on Bayes Risk in Decentralized Estimation (2016, background): “We ﬁrst lower-bound this quantity according to the estimation performance, such as the probability of excess distortion or the expected distortion.”
Inference Under Information Constraints II: Communication Constraints and Shared Randomness (2019, background): “Distributed hypothesis testing and estimation problems were first studied in information theory, although in a different setting than what we consider [6], [30], [31].”
Interactive hypothesis testing against independence (2013, background): “A more comprehensive survey on the earlier literature can be found in [5].”
Minimax Quantization for Distributed Maximum Likelihood Estimation (2006, background): “Data compression for distributed estimation has also been considered from an information theoretic perspective [8], where nodes encode long sequences of observations.”
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, unknown): “When the dimension of the parameter to be estimated is smaller than that of the observations, for example, in the case of HT, the observer can perform local inference and transmit its decision (indeed, optimal performance can be achieved asymptotically at zero rate by conveying the type information,”
Optimality guarantees for distributed statistical estimation (2014, background): “• Information theorists have also studied problems of distributed estimation; for instance, see the paper of Han and Amari [17] for an overview.”
Information-theoretic lower bounds for distributed statistical estimation with communication constraints (2013, unknown): “, [18, 16, 15]) and information theory [11].”
Extra Samples can Reduce the Communication for Independence Testing (2018, background): “In contrast, the available information theory literature on distributed independence testing (see [12] for a survey) pri-”
Distributed Signal Detection under Communication Constraints (2020, methodology): “Finally, distributed inference under communication constraints was also studied in the information theory community (Ahlswede and Csisz´ar, 1986; Han, 1987; Han and Amari, 1998) to characterize the error exponents as a function of the communication rate.”
Information geometry on hierarchy of probability distributions (2001, background): “…not only for mathematical foundations of statistical inferences ([3], [12], [28] and many others) but also applied to information theory [5], [11], [25], [18], neural networks [6], [7], [9], [13], systems theory [4], [32], mathematical programming [33], statistical physics [10], [16], [38], and…”
On Optimal Data Compression in Multiterminal Statistical Inference (2011, background): “Since then, a considerable number of researchers, including T. Berger himself, Z. Zhang, R. Ahlswede, S. Amari, M. Burnashev, I. Csiszár, T. S. Han, K. Kobayashi, H. Shalaby and A. Papamarcou, have studied problems of multiterminal statistical inference ([1]–[11]).”
Testing against independence with multiple decision centers (2016, background): “More work on this distributed hypothesis testing problem can be found in [3], [4], and a comprehensive literature review is given by [5].”