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The likelihood ratio theory contributes tremendous success to para-metric inferences. Yet, there is no general applicable approach for non-parametric inferences based on function estimation. Maximum likelihood ratio test statistics in general may not exist in nonparametric function estimation setting. Even if they exist, they are hard to find and can not be(More)
Time-homogeneous diffusion models have been widely used for describing the stochastic dynamics of the underlying economic variables. Recently, Stanton proposed drift and diffusion estimators based on a higher-order approximation scheme and kernel regression method. He claimed that " higher order approximations must outperform lower order approximations "(More)
In an effort to capture the time variation on the instantaneous return and volatility functions, a family of time-dependent diffusion processes is introduced to model the term structure dynamics. This allows one to examine how the instantaneous return and price volatility change over time and price level. Nonparametric techniques, based on kernel(More)
Many applications of nonparametric tests based on curve estimation involve selecting a smoothing parameter. The author proposes an adaptive test that combines several generalized likelihood ratio tests in order to get power performance nearly equal to whichever of the component tests is best. He derives the asymptotic joint distribution of the component(More)
The emergence of embedded and multimedia applications, which have a data-centric favor to them, have great influences on the design methodology of future systems. The 2D FFT is of particular importance to these applications. In this paper, leveraging the reconfigurable features of off-the-shelf FPGAs, we propose a stream architecture that is suitable for(More)
Event-related functional magnetic resonance imaging (efMRI) has emerged as a powerful technique for detecting brains' responses to presented stimuli. A primary goal in efMRI data analysis is to estimate the Hemodynamic Response Function (HRF) and to locate activated regions in human brains when specific tasks are performed. This paper develops new(More)
The explosion of Next Generation Sequencing (NGS) data with over one billion reads per day poses a great challenge to the capability of current computing systems. In this paper, we proposed a CPU-FPGA heterogeneous architecture for accelerating a short reads mapping algorithm, which was built upon the concept of hash-index. In particular, by extracting and(More)
Functional magnetic resonance imaging (fMRI) is emerging as a powerful tool for studying the process underlying the working of the many regions of the human brain. The standard tool for analyzing fMRI data is some variant of the linear model, which is restrictive in modeling assumptions. In this paper, we develop a semiparametric approach, based on the(More)