38 research outputs found
A General Formula for the Mismatch Capacity
The fundamental limits of channels with mismatched decoding are addressed. A
general formula is established for the mismatch capacity of a general channel,
defined as a sequence of conditional distributions with a general decoding
metrics sequence. We deduce an identity between the Verd\'{u}-Han general
channel capacity formula, and the mismatch capacity formula applied to Maximum
Likelihood decoding metric. Further, several upper bounds on the capacity are
provided, and a simpler expression for a lower bound is derived for the case of
a non-negative decoding metric. The general formula is specialized to the case
of finite input and output alphabet channels with a type-dependent metric. The
closely related problem of threshold mismatched decoding is also studied, and a
general expression for the threshold mismatch capacity is obtained. As an
example of threshold mismatch capacity, we state a general expression for the
erasures-only capacity of the finite input and output alphabet channel. We
observe that for every channel there exists a (matched) threshold decoder which
is capacity achieving. Additionally, necessary and sufficient conditions are
stated for a channel to have a strong converse. Csisz\'{a}r and Narayan's
conjecture is proved for bounded metrics, providing a positive answer to the
open problem introduced in [1], i.e., that the "product-space" improvement of
the lower random coding bound, , is indeed the mismatch
capacity of the discrete memoryless channel . We conclude by presenting an
identity between the threshold capacity and in the DMC
case
On the non-existence of unbiased estimators in constrained estimation problems
We address the problem of existence of unbiased constrained parameter estimators. We show that if the constrained set of parameters is compact and the hypothesized distributions are absolutely continuous with respect to one another, then there exists no unbiased estimator.Weaker conditions for the absence of unbiased constrained estimators are also specified. We provide several examples which demonstrate the utility of these conditions