Parameter estimation by contrast minimization for noisy observations of a diffusion process

We consider the estimation of unknown parameters in the drift and diffusion coefficients of a one-dimensional ergodic diffusion X when the observation Y is a discrete sampling of X with an additive noise, at times i δ, i=1,?…?, N. Assuming that the sampling interval tends to 0 while the total length-time interval tends to infinity, we prove limit theorems for functionals associated with the observations, based on local means of the sample. We apply these results to obtain a contrast function. The associated minimum contrast estimators are shown to be consistent. Some examples are discussed with numerical simulations.     

A simulation testing and analysis of aggregate production planning strategies

In this study, a hybrid discrete event simulation (DES) and system dynamics (SD) methodology is applied to model and simulate aggregate production planning (APP) problem for the first time. DES is used to simulate operational-level and shop-floor activities incorporated into APP and estimate critical time-based control parameters used in SD model of APP and SD is used to simulate APP as a collection of aggregate-level strategic decisions. The main objective of this study is to determine and analyse the effectiveness of APP strategies regarding the Total Profit criterion by developing a hybrid DES–SD simulation model for APP in a real-world manufacturing company. The simulation results demonstrated that the priority of APP strategies with regards to Total Profit criterion is: (1) the pure chase strategy, (2) the modified chase strategy, (3) the pure level strategy, (4) the modified level strategy, (5) the mixed strategy and (6) the demand management strategy, respectively. The APP system is first simulated under mixed strategy (basic scenario) conditions to include all APP capacity and demand options in constructed SD simulation model to show a comprehensive view of APP components and their interdependent interactions. Then, the obtained results will be used as Total Profit measure to compare with system's performance under some experimental scenarios applying different APP strategies.     

Adaptive Bayes sum test for the equality of two nonparametric functions

The statistical difference among massive data sets or signals is of interest to many diverse fields including neurophysiology, imaging, engineering, and other related fields. However, such data often have nonlinear curves, depending on spatial patterns, and have non-white noise that leads to difficulties in testing the significant differences between them. In this paper, we propose an adaptive Bayes sum test that can test the significance between two nonlinear curves by taking into account spatial dependence and by reducing the effect of non-white noise. Our approach is developed by adapting the Bayes sum test statistic by Hart [13 J.D. Hart, Frequentist-Bayes lack-of-fit tests based on Laplace approximations, J. Stat. Theory Practice 3 (2009), pp. 681–704. doi: 10.1080/15598608.2009.10411954[Taylor &; Francis Online] , [Google Scholar]]. The spatial pattern is treated through Fourier transformation. Resampling techniques are employed to construct the empirical distribution of test statistic to reduce the effect of non-white noise. A simulation study suggests that our approach performs better than the alternative method, the adaptive Neyman test by Fan and Lin [9 J. Fan and S. Lin, Test of significance when data are curves, J. Amer. Math. Soc. 93 (1997), pp. 1007–1021.[Web of Science ®] , [Google Scholar]]. The usefulness of our approach is demonstrated with an application in the identification of electronic chips as well as an application to test the change of pattern of precipitations.     

Some distributions arising from faulty inspection with multitype defectives,and an application to grading

Continuing their investigation of distributions arising from faulty and "incomplete" inspection from a finite popu-lation (Johnson et al. (1980), Comm. Statist.A9 917-922, Kotz and Johnson, ibid, All, 1997-2016) the authors investigate cases when inspection is not restricted to a single type of defect. Applications to grading problems, when grading is based on sets of defects found by sampling inspections, are indicated.     

A new discrete distrlbution,with applications to survival,dispersal and dispersion

We describe a new discrete probability distribution with several useful properties for the analysis and modelling of survival processes and dispersion. First, the model can be used to describe survival processes with monotonically decreasing, constant, or increasing hazard functions, simply by tuning one parameter. Also, the model can describe counts that are overdispersed (contagious) or underdispersed, since the variance can exceed, equal, or be less than the mean. All of these properties are demonstrated both theoretically and with ecological examples, using ad-hoc parameter estimation techniques. Finally, the equations are tractable compared with, say, the negative binomial, and easily incorporated into larger models.     

A Review on Characterizations of Discrete Distributions Based on Records and kth Records

This is a survey of characterizations of discrete distributions via properties of record values. Characterization results based on records and weak records are presented. Then the concepts of kth records, strong kth records, and weak kth records are recalled. Finally, characterizations of the geometric parent involving these three types of kth records are discussed.     

Comparison of cumulative incidence functions of current status competing risks data with discrete observation times

Abstract

Competing risks data with current status censoring arise frequently from transversal studies in demography, epidemiology and reliability theory; where the only information about lifetime is whether the event of interest has occurred or not before a monitoring time. In practice, the monitoring times are discrete, but most of the studies consider them as continuous in nature. In the present paper, we propose a non parametric test for comparing cumulative incidence functions of current status competing risks data while the observation (monitoring) times are discrete. Asymptotic distribution of the test statistic is also derived. A simulation study is conducted to assess the finite sample behavior of the test statistic. The practical utility of the procedure is well demonstrated using a real-life data set on menopausal history of 2423 women given in Jewell, van der Laan, and Henneman (2003 Jewell, N. P., M. van der Laan, and T. Henneman. 2003. Nonparametric estimation from current status data with competing risks. Biometrika 90 (1):183–197. doi: 10.1093/biomet/90.1.183.[Crossref], [Web of Science ®] , [Google Scholar]).     

Undercoverage of Wavelet-Based Resampling Confidence Intervals

The decorrelating property of the discrete wavelet transformation (DWT) appears valuable because one can avoid estimating the correlation structure in the original data space by bootstrap resampling of the DWT. Several authors have shown that the wavestrap approximately retains the correlation structure of observations. However, simply retaining the same correlation structure of original observations does not guarantee enough variation for regression parameter estimators. Our simulation studies show that these wavestraps yield undercoverage of parameters for a simple linear regression for time series data of the type that arise in functional MRI experiments. It is disappointing that the wavestrap does not even provide valid resamples for both white noise sequences and fractional Brownian noise sequences. Thus, the wavestrap method is not completely valid in obtaining resamples related to linear regression analysis and should be used with caution for hypothesis testing as well. The reasons for these undercoverages are also discussed. A parametric bootstrap resampling in the wavelet domain is introduced to offer insight into these previously undiscovered defects in wavestrapping.     

抛物线平方变迹光纤光栅中布拉格亮孤子传输特性的数值研究

本文在耦合模理论的基础上,运用有限差分法数值模拟了布拉格亮孤子在抛物线平方变迹光纤光栅中的传输特性．结果表明,输入功率和初始脉宽等初始条件的不同将导致孤子峰值强度的周期变化;合适选择输入功率和初始脉宽,可使光孤子在变迹光纤光栅中稳定传输．孤子在传输过程中始终受到抛物线平方变迹布拉格光栅的制约．     

基于MODWT的FIGARCH模型波动的持续性与相关性

基于FIGARCH模型与最大重复离散小波变换的小波多分辨分析,提出了不同尺度下的FIGARCH模型,并以此来讨论金融波动序列在不同尺度下的持续性问题;利用可描述两列波动序列的整体相关特性的交叉互相关函数,提出了可以描述两列波动序列在不同尺度、不同滞后期下小波交叉互相关函数的定义,并进行了实证分析。