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The article deals with the scalar-valued score functionSFdefined for a regular unimodal and continuous probability distributionFwith arbitrary interval support, recently introduced by the author. The concept of the central characteristic that describes a relative influence ofis described in a much more general way in order to improve its usefulness in parametric estimation by means of the general moment method. In particular, the whole approach is elucidated by describing it in a more suitable framework than before. Further, we show that the inference function to be used in the moment estimating equations is either the score function of distribution (bounded for heavy-tailed parametric distribution models) or its modification based on the Huber’s approach. [ABSTRACT FROM AUTHOR]

We provide the theoretical justification of bootstrapping stationary invertible echelon vector autoregressive moving-average (VARMA) models using linear methods. The asymptotic validity of the bootstrap is established with strong white noise under parametric and nonparametric assumptions. Our methods are practical and useful for building reliable simulation-based inference and forecasting without implementing nonlinear estimation techniques such as ML which is usually burdensome, time demanding or impractical, particularly in big or highly persistent systems. The relevance of our procedures is more pronounced in the context of dynamic simulation-based techniques such as maximized Monte Carlo (MMC) tests [see Dufour J-M. Monte Carlo tests with nuisance parameters: a general approach to finite-sample inference and nonstandard asymptotics in econometrics. J Econom. 2006;133(2):443–477 and Dufour J-M, Jouini T. Finite-sample simulation-based tests in VAR models with applications to Granger causality testing. J Econom. 2006;135(1–2):229–254 for the VAR case]. Simulation evidence shows that, compared with conventional asymptotics, our bootstrap methods have good finite-sample properties in approximating the actual distribution of the studentized echelon VARMA parameter estimates, and in providing echelon parameter confidence sets with satisfactory coverage. [ABSTRACT FROM PUBLISHER]

Introduced in the 1980s, value at risk has been a popular measure of financial risk. However, value at risk suffers from a number of drawbacks as measure of financial risk. An alternative measure referred to asexpected shortfallwas introduced in late 1990s to circumvent these drawbacks. Much theory have been developed since then. The developments have been most intensive in recent years.However, we are not aware of any comprehensive review of known estimation methods for expected shortfall. We feel it is timely that such a review is written. This paper (containing six sections and over 140 references) attempts that task with emphasis on recent developments. We expect this review to serve as a source of reference and encourage further research with respect to measures of financial risk. [ABSTRACT FROM AUTHOR]

This paper discusses the development of pedotransfer functions (PTFs) and uses a multiple nonlinear regression technique to validate point and parametric PTFs for the estimation of a water-retention curve from basic soil properties such as particle-size distribution, bulk density and organic matter content. One hundred soil samples were collected at different depths from different locations in the Pavanje river basin that lies within the southern coastal region of Karnataka, India. Prediction accuracies were evaluated using the coefficient of determination (R2), root mean square error (RMSE) and mean error (ME) between measured and predicted values. Overall, both point and parametric methods predicted water contents at selected water potentials with considerable accuracy. However, prediction of the soil water-retention curve using PTFs by point estimation method was relatively more successful (best caseR2 = 0.983) for the sampled soils.F-tests were also conducted for all cases. For one regression equation, thep-value was zero and for other equation, values were close to zero. Critical comparative analysis was carried out on the performances of the point and parametric methods. Use of the developed PTFs is suggested for the prediction of a water-retention curve for loamy sand and sandy loam soils in this area of the coastal region of southern India. [ABSTRACT FROM AUTHOR]

Several researchers considered various interval estimators for estimating the mean of a skewed distribution. Since they considered in different times and under different simulation conditions, their performance are not comparable as a whole. In this article, an attempt has been made to review some existing estimators and compare them under the same simulation condition. In particular, we consider and compare both classical (Student-t, Land-t, Cheb-t, Johnson-t, Chen-t, Hall-t, median-t, Zhou and Dinh, empirical likelihood, etc.) and nonparametric (bootstrap-t, nonparametric bootstrap, empirical likelihood bootstrap, bias corrected acceleration bootstrap, Hall bootstrap-t, empirical Hall bootstrap, etc.) interval estimators for estimating the mean of a positively skewed distribution. A simulation study has been made to compare the performance of the estimators. Both average widths and coverage probabilities are considered as a criterion of the good estimators. Under the large sample sizes, the performances of the estimators are not different. However, they differ significantly when the sample sizes are small and data are from a highly skewed distribution. Some real-life data have been analyzed to illustrate the findings of the article. Based on the simulation study, some possible good interval estimators have been recommended for the practitioners. This article will provide more choices for the practitioners to use best possible interval estimators among many that have been used by several researchers at different times and situations. [ABSTRACT FROM AUTHOR]

Application of simulated annealing (SA) and evolutionary algorithm (EA) techniques for optimization of parameters in the parametric quantum chemistry method (CATIVIC) was performed. A set of organic molecules and gold clusters were selected for test these techniques. Comparison was performed with respect to experimental values and DFT calculated equilibrium bond distances (EBD) and bond angles (BA). Results show that EA is more efficient than SA with respect to computer time. Accuracy is similar in both methods; however, there are important differences in the set of parameters. [ABSTRACT FROM AUTHOR]

We introduce a dynamic formulation for the problem of portfolio selection of pension funds in the absence of a risk-free asset. In emerging markets, a risk-free asset might be unavailable, and the approaches commonly used may no longer be suitable. We use a parametric approach to combine dynamic programming and Monte Carlo simulation to gain additional flexibility. This approach is general in the sense that optimal asset allocation is tractable for all HARA utility functions in the absence of a risk-free asset. The traditional case composed of several risky assets and one risk-free asset is compared to a case in which the risk-free asset is unavailable. [ABSTRACT FROM PUBLISHER]