Nonlinear and nonnormal filters are introduced and developed. Traditional nonlinear filters such as the extended Kalman filter and the Gaussian sum filter give biased filtering estimates, and therefore several nonlinear and nonnormal filters have been derived from the underlying probability...

For a nonlinear filtering problem, the most heuristic and easiest approximation is to use the Taylor series expansion and apply the conventional linear recursive Kalman filter algorithm directly to the linearized nonlinear measurement and transition equations. First, it is discussed that the...

Purpose: The purpose of this paper is to investigate how the selection of return distribution impacts estimated volatility in China’s stock market. Design/methodology/approach: The authors use a Bayesian analysis of fat-tailed stochastic volatility (SV) model with Student’s t-distribution,...

Purpose: The purpose of this paper is to examine the day-of-the-week effects of Bitcoin (BTC) markets on the exchange level from January 2014 to September 2018. Design/methodology/approach: The in-depth study on the day-of-the-week effects is conducted by using data consisting of Bitcoin prices...

This paper applies a bootstrap method to the Almost Ideal Demand System (AIDS) proposed by Deaton and Muellbauer (Am Econ Rev 70:312–326, <CitationRef CitationID="CR5">1980</CitationRef>), where the moving blocks bootstrap (MBB) and pairs bootstrap (PB) methods are adopted taking into account serially correlated error terms and limited...</citationref>

Non-parametric tests that deal with two samples include scores tests (such as the Wilcoxon rank sum test, normal scores test, logistic scores test, Cauchy scores test, etc.) and Fisher's randomization test. Because the non-parametric tests generally require a large amount of computational work,...