Ethnic distribution, effective power and conflict

This paper highlights the fact that different distributional aspects of ethnicity matter for conflict. We axiomatically derive a parametric class of indices of conflict potential obtained as the sum of each ethnic group’s relative power weighted by the probability of across group interactions. The power component of an extreme element of this class of indices is given by the Penrose–Banzhaf measure of relative power. This index combines in a non-linear way fractionalization, polarization and dominance. The empirical analysis verifies that it outperforms the existing indices of ethnic diversity in explaining ethnic conflict onset.

Specification testing in semi-parametric transformation models

In transformation regression models, the response is transformed before fitting a regression model to covariates and transformed response. We assume such a model where the errors are independent from the covariates and the regression function is modeled nonparametrically. We suggest a test for goodness-of-fit of a parametric transformation class based on a distance between a nonparametric transformation estimator and the parametric class. We present asymptotic theory under the null hypothesis of validity of the semi-parametric model and under local alternatives. A bootstrap algorithm is suggested in order to apply the test. We also consider relevant hypotheses to distinguish between large and small distances of the parametric transformation class to the ‘true’ transformation.

A new parametric class of solutions of a charged anisotropic compact star via Bardeen exterior geometry

In this paper, we provide a new parametric class of solutions to Einstein–Maxwell field equations to study the relativistic structure of a compact star via embedding class I condition. The interior of the star is delineated by Karmarkar condition and at the boundary of the star, we match the class of solutions with Bardeen and Reissner–Nordstrom exterior spacetimes. We assume one of the metric potentials as [Formula: see text] to obtain other metric potential. Subsequently, we solve Maxwell field equations. We verify and compare all the thermodynamic properties like matter density, anisotropy, radial and tangential pressures, compactification factor, energy conditions, and stability conditions, namely, adiabatic index, balancing forces via modified TOV equations, Harrision–Zeldovich criteria, casualty condition, Herrera cracking condition, etc., of our class of charged solutions. All the physical and stability conditions are with the viable trend throughout the interior of the stellar object. For a suitable range of values of [Formula: see text] and parameters, it is depicted from this study that the present class of charged solutions yields effective results to obtain realistic and viable modeling of the neutron star in EXO 1785-248 in both the Bardeen and Reissner–Nordstrom exterior spacetimes.

Convergence and Stability of a Parametric Class of Iterative Schemes for Solving Nonlinear Systems

A new parametric class of iterative schemes for solving nonlinear systems is designed. The third- or fourth-order convergence, depending on the values of the parameter being proven. The analysis of the dynamical behavior of this class in the context of scalar nonlinear equations is presented. This study gives us important information about the stability and reliability of the members of the family. The numerical results obtained by applying different elements of the family for solving the Hammerstein integral equation and the Fisher’s equation confirm the theoretical results.

The Choice Function Framework for Online Policy Improvement

There are notable examples of online search improving over hand-coded or learned policies (e.g. AlphaZero) for sequential decision making. It is not clear, however, whether or not policy improvement is guaranteed for many of these approaches, even when given a perfect leaf evaluation function and transition model. Indeed, simple counterexamples show that seemingly reasonable online search procedures can hurt performance compared to the original policy. To address this issue, we introduce the choice function framework for analyzing online search procedures for policy improvement. A choice function specifies the actions to be considered at every node of a search tree, with all other actions being pruned. Our main contribution is to give sufficient conditions for stationary and non-stationary choice functions to guarantee that the value achieved by online search is no worse than the original policy. In addition, we describe a general parametric class of choice functions that satisfy those conditions and present an illustrative use case of the empirical utility of the framework.

Relativistic modeling of Vela X-1 using the Karmarkar condition

The objective of this work is to explore a new parametric class of exact solutions of the Einstein field equations coupled with the Karmarkar condition. Assuming a new metric potential [Formula: see text] with parameter (n), we find a parametric class of solutions which is physically well-behaved and represents compact stellar model of the neutron star in Vela X-1. A detailed study specifically shows that the model actually corresponds to the neutron star in Vela X-1 in terms of the mass and radius. In this connection, we investigate several physical properties like the variation of pressure, density, pressure–density ratio, adiabatic sound speeds, adiabatic index, energy conditions, stability, anisotropic nature and surface redshift through graphical plots and mathematical calculations. All the features from these studies are in excellent conformity with the already available evidences in theory. Further, we study the variation of physical properties of the neutron star in Vela X-1 with the parameter (n).

Efficiency of Average Treatment Effect Estimation When the True Propensity Is Parametric

It is well known that efficient estimation of average treatment effects can be obtained by the method of inverse propensity score weighting, using the estimated propensity score, even when the true one is known. When the true propensity score is unknown but parametric, it is conjectured from the literature that we still need nonparametric propensity score estimation to achieve the efficiency. We formalize this argument and further identify the source of the efficiency loss arising from parametric estimation of the propensity score. We also provide an intuition of why this overfitting is necessary. Our finding suggests that, even when we know that the true propensity score belongs to a parametric class, we still need to estimate the propensity score by a nonparametric method in applications.

DYNAMICAL ANALYSIS TO EXPLAIN THE NUMERICAL ANOMALIES IN THE FAMILY OF ERMAKOV-KALITLIN TYPE METHODS

In this paper, we study the dynamics of an iterative method based on the Ermakov-Kalitkin class of iterative schemes for solving nonlinear equations. As it was proven in ”A new family of iterative methods widening areas of convergence, Appl. Math. Comput.”, this family has the property of getting good estimations of the solution when Newton’s method fails. Moreover, the set of converging starting points for several non-polynomial test functions was plotted and they showed to be wider in the case of proposed methods than in Newton’s case, for small values of the parameter. Now, we make a complex dynamical analysis of this parametric class in order to justify the stability properties of this family.