Computational Statistics is an international journal that fosters the publication of applications and methodological research in the field of computational statistics. In this article, we will discuss the motivation, history, some specialties, and the future scope of this journal.

Along with many others, we agree that a modern education in statistics needs to incorporate the practical analysis of real datasets, which are usually more complex than the common examples found in standard textbooks. The software used in the teaching of statistics includes standard spreadsheet...

State price density (SPD) contains important information concerning market expectations. In existing literature, a constrained estimator of the SPD is found by nonlinear least squares in a suitable Sobolev space. We improve the behavior of this estimator by implementing a covariance structure...

An enormous number of statistical methods have been developed in quantitive finance during the last decades. Nonparametric methods, bootstrapping time series, wavelets, estimation of diffusion coefficients are now almost standard in statistical applications. To implement these new methods the...

In this study a framework for an online database-driven repository of information – QuantNet – is presented. QuantNet is aimed at easing the process of web publishing for those who are unfamiliar with technical details and markup languages. At the same time advanced users are provided with...

The influence of maternal health problems on child’s worrying status is important in practice in terms of the intervention of maternal health problems early for the influence on child’s worrying status. Conventional methods apply symmetric prior distributions such as a normal...

This paper is intended as a guide to building insurance risk (loss) models. A typical model for insurance risk, the so-called collective risk model, treats the aggregate loss as having a compound distribution with two main components: one characterizing the arrival of claims and another...

Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of many kind of high-dimensional data. It is used in signal process- ing, mechanical ingeneering, psychometrics, and other fields under different names. It still bears the same mathematical idea: the...

I compare the performance of solution methods in solving a standard real business cycle model with labor market search frictions. Under the conventional calibration, the model is solved by the projection method using the Chebyshev polynomials as its basis, and the perturbation methods up to...

We propose a semiparametric measure to estimate systemic interconnectedness across financial institutions based on tail-driven spill-over effects in a ultra-high dimensional framework. Methodologically, we employ a variable selection technique in a time series setting in the context of a...