A Better Approach to Resolving Variable Selection Uncertainty in Meta Analysis for Benefits Transfer

Because original high-quality non-market valuation studies can be expensive, perhaps prohibitively so, benefits transfer (BT) approaches are often used for valuing, e.g., the outputs of multifunctional agriculture. Here we focus on the use of BT functions, a preferred method, and address an under-appreciated problem – variable selection uncertainty – and demonstrate a conceptually superior method of resolving it. We show that the standard method of value-function BT, using the full estimated model, may generate BT values that are too sensitive to insignificant variables, whereas models reduced by backward elimination of insignificant variables pay no attention to insignificant variables that may in fact have some influence on values. Rather than searching for the best single model for BT, Bayesian model averaging (BMA) is attentive to all of the variables that are a priori relevant, but uses posterior model probabilities to give systematically lower weight to less significant variables. We estimate a full value model for wetlands in the US, and then calculate BT values from the full model, a reduced model, and by BMA. Variable selection uncertainty is exemplified by regional variables for wetland location. Predicted values from the full model are quite sensitive to region; reduced models pay no attention to regional variables; and the BMA predictions are attentive to region but give it relatively low weight. However, the suite of insignificant RHS variables, taken together, have non-trivial influence on BT values. BMA predicted values, like values from reduced models, have much narrower confidence intervals than values calculated from the full model.Research Methods/ Statistical Methods,

Nucleon sea in the effective chiral quark model

The asymmetries of both light-flavor antiquark $\bar{d}(x)-\bar{u}(x)$ and strange-antistrange $s(x)-\bar{s}(x)$ distributions of the nucleon sea are considered with more details in the effective chiral quark model. We find that the asymmetric distribution of light-flavor antiquarks $\bar{d}(x)-\bar{u}(x)$ matches the experiment data well and that the asymmetry of strange and antistrange distributions can bring about 60-100% correction to the NuTeV anomaly of $\sin^{2}\theta_{w}$, which are three standard deviations from the world average value measured in other electroweak processes. The results on the correction to the NuTeV anomaly are insensitive to the inputs of the constituent quark distributions and the cut-off parameters. The ratios of $\bar{d}(x)/\bar{u}(x)$ and $s(x)/\bar{s}(x)$ are also discussed, and it is found that the ratio $s(x)/\bar{s}(x)$ is compatible with the available experiments with an additional symmetric sea contribution being considered effectively.Comment: 24 Latex pages, 8 figure

Meta Analysis for Benefits Transfer – Toward Value Estimates for Some Outputs of Multifunctional Agriculture

As a contribution to valuing the outputs of multifunctional agriculture, we report three new meta analyses estimating value functions for agricultural conservation program impacts on water quality, wetlands, and upland habitat and open space. As is often the case in valuation, where methods have yet to be standardized, the data sets are relatively small and noisy. With a clear objective of benefits transfer, we seek robust parameter estimates for key RHS variables, even at the cost of some loss of goodness of fit. We present our estimated full equations, and benefits transfer values calculated from equations estimated after backward elimination of insignificant variables, and offer a rationale for this approach to benefits transfer.meta analysis, benefits transfer, multifunctional agriculture, Resource /Energy Economics and Policy,

LocNet: Global localization in 3D point clouds for mobile vehicles

Global localization in 3D point clouds is a challenging problem of estimating the pose of vehicles without any prior knowledge. In this paper, a solution to this problem is presented by achieving place recognition and metric pose estimation in the global prior map. Specifically, we present a semi-handcrafted representation learning method for LiDAR point clouds using siamese LocNets, which states the place recognition problem to a similarity modeling problem. With the final learned representations by LocNet, a global localization framework with range-only observations is proposed. To demonstrate the performance and effectiveness of our global localization system, KITTI dataset is employed for comparison with other algorithms, and also on our long-time multi-session datasets for evaluation. The result shows that our system can achieve high accuracy.Comment: 6 pages, IV 2018 accepte

Perturbation Theory of Single Particle Spectrum of Antiferromagnetic Mott Insulating States in the Hubbard Models

In this work, we present an analytical framework for studying antiferromagnetic (AFM) Mott insulating states in the Hubbard model. We first derive an analytical solution for the single-particle Green's functions in the atomic limit. Within a second-order perturbation approach, we compute the ground state energy and show that the ground state is antiferromagnetically ordered. Then we derive an analytical solution for single-particle Green's functions when effects of the hopping term are considered in the N\'{e}el state. With the analytical solution, we compute and explain various properties of antiferromagnetic Mott insulators observed both experimentally and numerically: i) magnetic blueshift of the Mott gap; ii) spectral functions with features comparable to observations by angle-resolved photoemission spectroscopy on parental compounds of cuprate high $T_c$ superconductors. This work comprehends the electronic properties of antiferromagnetic Mott states analytically and provides a foundation for future investigations of doped antiferromagnetic Mott insulators, aiming for the mechanism of cuprates high-$T_c$ superconductivity.Comment: 4.5 pages, 2 figure