一种用于DS-CDMA基站的全数字非相干延迟锁相环

本文根据直接序列扩频码分多址(DS-CDMA)系统上行链路伪随机码跟踪的特点,给出并分析一种全数字非相干延迟锁相环 (DLL),该DLL采用了二元鉴相和数字序贯滤波的实现结构。文中推导了多用户环境下环路的数学模型及鉴相误差统计特性,给出了跟踪性能的计算机仿真结果。研究结果表明,本文给出的DLL能以小的复杂度实现良好的跟踪性能,具有较高的应用价值。     

基于贝塞尔曲线的中、美、日利率期限结构静态研究

利率期限结构曲线的静态拟合是指,使用不同类型的数学函数近似地描述整条利率期限结构曲线。当前最流行的静态拟合方法是利用B样条曲线来拟合利率曲线。然而,该方法往往受制于阶数的限制,而仅仅停留在3阶。本文通过利用B样条曲线的特殊形式——Bezier曲线拟合了中国、美国、日本国债利率的期限结构曲线,获得了一种可以升阶的拟合方法。同时,将复杂的曲线拟合计算,简化为对散点的聚类分析,取得了中国利率期限结构的模型。     

Network-based spatial interpolation of commuting trajectories: application of a university commuting management project in Kyoto,Japan

This study presents an application of network-based spatial interpolation of student commuting trajectories from a series of origin–destination trip datasets. In particular, we incorporated multimodal public transportation networks, including bus networks, to estimate the student commuting routes. The student samples for this study were collected from an online travel diary survey conducted by Ritsumeikan University in Kyoto, Japan. The ArcGIS Network Analyst was used to construct spatial network datasets and reconstruct trajectories from the origin–destination trip dataset. In addition, line densities of estimated trajectories were calculated and displayed on maps for geovisualization. These maps helped us understand the precise locations of congestion and spatial patterns of student commuting, unlike linear representations of people’s movements that connect origins and destinations. Our study also showed that estimated trajectories can simulate quantitative impacts on travel time by promoting walking or the use of public transportation.     

A Bayesian Approach to Parameter Estimation in the Presence of Spatial Missing Data

The missing data problem has been widely addressed in the literature. The traditional methods for handling missing data may be not suited to spatial data, which can exhibit distinctive structures of dependence and/or heterogeneity. As a possible solution to the spatial missing data problem, this paper proposes an approach that combines the Bayesian Interpolation method [Benedetti, R. & Palma, D. (1994) Markov random field-based image subsampling method, Journal of Applied Statistics, 21(5), 495–509] with a multiple imputation procedure. The method is developed in a univariate and a multivariate framework, and its performance is evaluated through an empirical illustration based on data related to labour productivity in European regions.     

Interpolation and backdating with a large information set

Existing methods for data interpolation or backdating are either univariate or based on a very limited number of series, due to data and computing constraints that were binding until the recent past. Nowadays large datasets are readily available, and models with hundreds of parameters are easily estimated. We model these large datasets with a factor model, and develop an interpolation method that exploits the estimated factors as an efficient summary of all available information. The method is compared with existing standard approaches from a theoretical point of view, by means of Monte Carlo simulations, and also when applied to actual macroeconomic series. The results indicate that our method is rather robust to model misspecification, although traditional multivariate methods also work well while univariate approaches are systematically outperformed. When interpolated series are subsequently used in econometric analyses, biases can emerge, but they are smaller with multivariate approaches, including factor-based ones.     

数字化语音减速保调播放插值算法及实现

对数字化语音减速保调播放问题进行了研究并提出了一种播值算法。实验表明，算法既保证了语音质量，又有良好的实时性。     

A Unified View of Signal Extraction, Benchmarking, Interpolation and Extrapolation of Time Series

Time series data are often subject to statistical adjustments needed to increase accuracy, replace missing values and/or facilitate data analysis. The most common adjustments made to original observations are signal extraction (e.g. smoothing), benchmarking, interpolation and extrapolation. In this article, we present a general dynamic stochastic regression model, from which most of these adjustments can be performed, and prove that the resulting generalized least square estimator is minimum variance linear unbiased. We extend current methods to include those cases where the signal follows a mixed model (deterministic and stochastic components) and the errors are autocorrelated and heteroscedastic.     

Efficient willow tree method for European-style and American-style moving average barrier options pricing

Moving average options are widely traded in financial markets, but exiting methods for pricing this type of option are too slow. This paper proposes two efficient willow tree methods for pricing European-style and American-style moving average barrier options (MABOs). We first solve the finite-dimensional partial differential equation model for discretely monitored MABOs by willow tree methods, and then compute the value of continuously monitored MABOs by Richardson’s two-point extrapolation. Our new willow tree method employs the interpolation error minimization technique to reduce complexity. The corresponding convergence rate and error bounds are also analyzed. It shows that our proposed methods can provide the same accuracy as the binomial tree approach and Monte Carlo simulation, but require much less computing time. The numerical experiments support our claims.     

Can the choice of interpolation method explain the difference between swap prices and futures prices?

The standard model linking the swap rate to the rates in a contemporaneous strip of futures interest rate contracts typically produces biased estimates of the swap rate. Institutional differences usually require some form of interpolation to be employed and may in principle explain this empirical result. Using Australian data, we find evidence consistent with this explanation and show that model performance is greatly improved if an alternative interpolation method is used. In doing so, we also provide the first published Australian evidence on the accuracy of the futures‐based approach to pricing interest rate swaps.