We study the effective degrees of freedom of the lasso in the framework of Stein’s unbiased risk estimation (SURE). We show that the number of nonzero coefficients is an unbiased estimate for the degrees of freedom of the lasso—a conclusion that requires no special assumption on the predictors. In addition, the unbiased estimator is shown to be asymptotically consistent. With these results on h...

In this paper, we focus on pilot-aided channel estimation methods in the context of OFDM-based WLANs with Multiple Transmit Multiple Receive (MTMR) antennas, and we compare several solutions together with several pilot designs in a realistic context. In particular, we take into account null subcarriers as specified in the WLANs standards. We consider channel estimation based on the Minimum Mean...

This paper presents a generic small scale channel model for ultra wideband wireless body area network communications. It is based on static on-body measurements in an anechoic chamber by using a vector network analyzer within a 2-8 GHz bandwidth. Two antenna types are used for the examination: dipole and double loop. From the existing data, a generic average channel impulse response (CIR) was e...

A method for the design of Peak Constrained Least Squares (PCLS) Finite duration Impulse Response (FIR) digital filters with low complexity Finite Precision Coefficients (FPC) based on Adams’ optimality criterion and an efficient local search is presented. Simple quantization of the infinite precision coefficients typically leads to filter designs which fail to meet the frequency response, Pass...

ABATRACT In general, it is difficult to find some large scale model only from a bottom up way viewpoint for complicated sound insultion systems like non-parallel double wall or sound-bridge type double wall. Furthermore, in the acual environment, the input fluctuates non-stationarily and the output is observed under contamination of the background noise. In this paper, for the above complicated...

The choice of generalized linear mixed models is difficult, because it involves the selection of both fixed and random effects. Classical criteria like Akaike’s information criterion (AIC) are often not suitable for the latter task, and others which are useful in linear mixed models are difficult to extend to the generalized case, especially for overdispersed data. A predictive leave-one-out cr...

Let Y be a Gaussian vector whose components are independent with a common unknown variance. We consider the problem of estimating the mean μ of Y by model selection. More precisely, we start with a collection S = {Sm, m ∈M} of linear subspaces of R and associate to each of these the least-squares estimator of μ on Sm. Then, we use a data driven penalized criterion in order to select one estimat...

Let Y be a Gaussian vector whose components are independent with a common unknown variance. We consider the problem of estimating the mean of Y by model selection. More precisely, we start with a collection S = {Sm, m ∈M} of linear subspaces of R and associate to each of these the least-squares estimator of μ on Sm. Then, we use a data driven penalized criterion in order to select one estimator...

Let Y be a Gaussian vector whose components are independent with a common unknown variance. We consider the problem of estimating the mean μ of Y by model selection. More precisely, we start with a collection S = {Sm,m ∈ M} of linear subspaces of Rn and associate to each of these the least-squares estimator of μ on Sm. Then, we use a data driven penalized criterion in order to select one estima...

The purpose of this article is to look at how information criteria, such as AIC and BIC, interact with the g%SD fit criterion derived in Waddell et al. (2007, 2010a). The g%SD criterion measures the fit of data to model based on a normalized weighted root mean square percentage deviation between the observed data and model estimates of the data, with g%SD = 0 being a perfectly fitting model. Ho...