Implications of the Muon g-2 result on the flavour structure of the lepton mass matrix

The confirmation of the discrepancy with the Standard Model predictions in the anomalous magnetic moment by the Muon g-2 experiment at Fermilab points to a low scale of new physics. Flavour symmetries broken at low energies can account for this discrepancy but these models are much more restricted, as they would also generate off-diagonal entries in the dipole moment matrix. Therefore, if we assume that the observed discrepancy in the muon $$g-2$$ g - 2 is explained by the contributions of a low-energy flavor symmetry, lepton flavour violating processes can constrain the structure of the lepton mass matrices and therefore the flavour symmetries themselves predicting these structures. We apply these ideas to several discrete flavour symmetries popular in the leptonic sector, such as $$\Delta (27)$$ Δ ( 27 ) , $$A_4$$ A 4 , and $$A_5 < imes \mathrm{CP}$$ A 5 ⋉ CP .

A Straight-Forward Method of Moments Procedure to Solve the Time Domain Integral Equation Applicable to PEC Bodies via Triangular Patch Modeling

In this work, a simple and straight-forward method of moments solution (MOM) procedure is presented to obtain the induced current distribution on an arbitrarily-shaped conducting body illuminated by a Gaussian plane wave directly in the time domain using a patch modeling approach. The method presented in this work, besides being stable, is also capable of handling multiple excitation pulses of varying frequency content incident from different directions in a trivial manner. The method utilizes standard Rao-Wilton-Glisson (RWG) functions and simple triangular functions for the space and time variables, respectively, for both expansion and testing. The method adopts conventional MOM and requires no further manipulation invariably needed in standard time-marching methods. The moment matrix generated via this scheme is a block-wise Toeplitz matrix and, hence, the solution is extremely efficient. The method is validated by comparing the results with the data obtained from the frequency domain solution. Several simple and complex numerical results are presented to validate the procedure.

Improved Average Estimation in Seemingly Unrelated Regressions

In this paper, we propose an efficient weighted average estimator in Seemingly Unrelated Regressions. This average estimator shrinks a generalized least squares (GLS) estimator towards a restricted GLS estimator, where the restrictions represent possible parameter homogeneity specifications. The shrinkage weight is inversely proportional to a weighted quadratic loss function. The approximate bias and second moment matrix of the average estimator using the large-sample approximations are provided. We give the conditions under which the average estimator dominates the GLS estimator on the basis of their mean squared errors. We illustrate our estimator by applying it to a cost system for United States (U.S.) Commercial banks, over the period from 2000 to 2018. Our results indicate that on average most of the banks have been operating under increasing returns to scale. We find that over the recent years, scale economies are a plausible reason for the growth in average size of banks and the tendency toward increasing scale is likely to continue

MaxSkew and MultiSkew: Two R Packages for Detecting, Measuring and Removing Multivariate Skewness

The R packages MaxSkew and MultiSkew measure, test and remove skewness from multivariate data using their third-order standardized moments. Skewness is measured by scalar functions of the third standardized moment matrix. Skewness is tested with either the bootstrap or under normality. Skewness is removed by appropriate linear projections. The packages might be used to recover data features, as for example clusters and outliers. They are also helpful in improving the performances of statistical methods, as for example the Hotelling’s one-sample test. The Iris dataset illustrates the usages of MaxSkew and MultiSkew.

Stabilised MLS in MLPG method for heat conduction problem

Purpose The purpose of this paper is to assess the performance of the stabilised moving least squares (MLS) scheme in the meshless local Petrov–Galerkin (MLPG) method for heat conduction method. Design/methodology/approach In the current work, the authors extend the stabilised MLS approach to the MLPG method for heat conduction problem. Its performance has been compared with the MLPG method based on the standard MLS and local coordinate MLS. The patch tests of MLS and modified MLS schemes have been presented along with the one- and two-dimensional examples for MLPG method of the heat conduction problem. Findings In the stabilised MLS, the condition number of moment matrix is independent of the nodal spacing and it is nearly constant in the global domain for all grid sizes. The shifted polynomials based MLS and stabilised MLS approaches are more robust than the standard MLS scheme in the MLPG method analysis of heat conduction problems. Originality/value The MLPG method based on the stabilised MLS scheme.