288,997 research outputs found
GAMLSS for high-dimensional data – a flexible approach based on boosting
Generalized additive models for location, scale and shape (GAMLSS) are a popular semi-parametric modelling approach that, in contrast to conventional GAMs, regress not only the expected mean but every distribution parameter (e.g. location, scale and shape) to a set of covariates. Current fitting procedures for GAMLSS are infeasible for high-dimensional data setups and require variable selection based on (potentially problematic) information criteria. The present work describes a boosting algorithm for high-dimensional GAMLSS that was developed to overcome these limitations. Specifically, the new algorithm was designed to allow the simultaneous estimation of predictor effects and variable selection. The proposed algorithm was applied to data of the Munich Rental Guide, which is used by
landlords and tenants as a reference for the average rent of a flat depending on its characteristics and spatial features. The net-rent predictions that resulted from the high-dimensional GAMLSS were found to be highly competitive while covariate-specific prediction intervals showed a major improvement over classical GAMs
Direct Nitrous Oxide Emissions From Tropical And Sub-Tropical Agricultural Systems : A Review and Modelling of Emission Factors
We acknowledge the financial support from the CGIAR Research Programs on Climate Change, Agriculture and Food Security (CCAFS). Grant ref. n. P25.Peer reviewedPublisher PD
How Much Do Downlink Pilots Improve Cell-Free Massive MIMO?
In this paper, we analyze the benefits of including downlink pilots in a
cell-free massive MIMO system. We derive an approximate per-user achievable
downlink rate for conjugate beamforming processing, which takes into account
both uplink and downlink channel estimation errors, and power control. A
performance comparison is carried out, in terms of per-user net throughput,
considering cell-free massive MIMO operation with and without downlink
training, for different network densities. We take also into account the
performance improvement provided by max-min fairness power control in the
downlink. Numerical results show that, exploiting downlink pilots, the
performance can be considerably improved in low density networks over the
conventional scheme where the users rely on statistical channel knowledge only.
In high density networks, performance improvements are moderate.Comment: 7 pages, 5 figures. IEEE Global Communications Conference 2016
(GLOBECOM). Accepte
Optimal Channel Training in Uplink Network MIMO Systems
We consider a multi-cell frequency-selective fading uplink channel (network
MIMO) from K single-antenna user terminals (UTs) to B cooperative base stations
(BSs) with M antennas each. The BSs, assumed to be oblivious of the applied
codebooks, forward compressed versions of their observations to a central
station (CS) via capacity limited backhaul links. The CS jointly decodes the
messages from all UTs. Since the BSs and the CS are assumed to have no prior
channel state information (CSI), the channel needs to be estimated during its
coherence time. Based on a lower bound of the ergodic mutual information, we
determine the optimal fraction of the coherence time used for channel training,
taking different path losses between the UTs and the BSs into account. We then
study how the optimal training length is impacted by the backhaul capacity.
Although our analytical results are based on a large system limit, we show by
simulations that they provide very accurate approximations for even small
system dimensions.Comment: 15 pages, 7 figures. To appear in the IEEE Transactions on Signal
Processin
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