Description The package implements the statistical theory of L-moments including L-moment estimation, probability-weighted moment estimation, parameter estimation for numerous familiar and not-so-familiar distributions, and L-moment estimation for the same distributions from the parameters. L-moments are derived from the expectations of order statistics and are linear with respect to the probab...

The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to th...

2. In practice, researchers find it useful that GMM estimators can be constructed without specifying the full data generating process (which would be required to write down the maximum likelihood estimator.) This has been the case in the study of single equations in a simultaneous system, in the study of potentially misspecified dynamic models designed to match target moments, and in the constr...

an evolutionary structural optimization (eso) method is used for plastic design of frames. based on safe theorems some criteria are derived and made an effort to satisfy them during the optimization process. in this regard, equilibrium is checked and yield condition is gradually satisfied during the optimization process. in this method, the amount of used material and the stiffness for each ele...

In this paper we give some recurrence relations satisfied by single and product moments of generalized order statistics from doubly truncated generalized exponential distribution. These relations are deduced for moments of record values and order statistics. Further, using a recurrence relation for single moments we obtain a characterization of generalized exponential distribution.

We provide an approach for learning deep neural net representations of models described via conditional moment restrictions. Conditional moment restrictions are widely used, as they are the language by which social scientists describe the assumptions they make to enable causal inference. We formulate the problem of estimating the underling model as a zero-sum game between a modeler and an adver...