A monolithic caisson is one of the major structural components commonly used in the construction of docks, breakwaters, and container terminal quay walls. Since the structure of monolithic caissons is typical and somewhat simple, its design process is also typical and repetitive, but still time consuming. To reduce the total time and enhance productivity, we developed a parametric model in Revi...

We present a parametric model for feather modeling in computer graphics. The model is based on Bézier curves and allows easy generation of many feather structures through manipulation of the parameters.

In medical, biological and social science, three-dimensional contingency tables with ordered categories usually arise and one expects a monotone association, yet one may prefer not to assume a particular parametric model. One of its key roles is how to estimate its parameters under monotonic restrictions. In this paper, we propose an algorithm to compute the maximum likelihood estimation under ...

Indirect inference estimators (i.e., simulation-based minimum distance estimators) in a parametric model that are based on auxiliary nonparametric maximum likelihood density estimators are shown to be asymptotically normal. If the parametric model is correctly specified, it is furthermore shown that the asymptotic variance-covariance matrix equals the inverse of the Fisher-information matrix. T...

This paper considers the problem of the achievable accuracy in jointly estimating the parameters of a complex-valued two-dimensional (2-D) Gaussian and homogeneous random field from a single observed realization of it. Based on the 2-D Wold decomposition, the field is modeled as a sum of purely indeterministic, evanescent, and harmonic components. Using this parametric model, we first solve a k...

We consider a Bayesian approach to goodness of fit, that is, to the problem of testing whether or not a given parametric model is compatible with the data at hand. We thus consider a parametric family F = fF ; 2 g ; where F denotes a cumulative distribution function with parameter . The null hypothesis is H0 : X F for an unknown , that is, there exists such that F (X) U(0; 1). If H0 does not ho...

Regression adjustments are often made to experimental data. Since randomization does not justify the models, almost anything can happen. Here, we evaluate results using Neyman’s non-parametric model, where each subject has two potential responses, one if treated and the other if untreated. Only one of the two responses is observed. Regression estimates are generally biased, but the bias is smal...

A new approach for using Lévy processes to compute value at risk (VaR) using high-frequency data is presented in this paper. The approach is a parametric model using an ARMA(1,1)-GARCH(1,1) model where the tail events are modeled using the fractional Lévy stable noise and Lévy stable distribution. Using high-frequency data for the German DAX Index, the VaR estimates from this approach are compa...

We propose a semiparametric single-factor diffusion model for the term structure of interest rate. The This model is highly flexible and encompasses most parametric single-factor models proposed in the literature. We fit the semiparametric model to a proxy of the Eurodollar short term interst rate and compare it with the most flexible parametric model found in the literature: First directly, by...

This paper proposes a block Arnoldi method for parameterized model order reduction. This method works when design parameters have only low-rank impacts on the system matrix. The method preserves all design parameters in the reduced model and is easy to implement. Numerical results show that the block Arnoldi process outperforms some existing methods up to a factor of ten.