Heteroscedasticity-robust estimation of autocorrelation

This paper proposes estimators of the first-order autocorrelation that are based on suitably transformed ratios of successive observations. The new estimators are given by simple functions of the observations. Numerical optimization is not required. Simulations show that they are highly robust against extreme values and clusters of high volatility and are therefore particularly useful for the estimation of serial correlation in return series. Besides, the results of the simulation study also call into question the common practice of correcting the small-sample bias of conventional estimators.     

Identifiable state‐space models: A case study of the Bay of Fundy sea scallop fishery

State‐space models (SSMs) are now popular tools in fisheries science for providing management advice when faced with noisy survey and commercial fishery data. Such models are often fitted within a Bayesian framework requiring both the specification of prior distributions for model parameters and simulation‐based approaches for inference. Here we present a frequentist framework as a viable alternative and recommend using the Laplace approximation with automatic differentiation, as implemented in the R package Template Model Builder, for fast fitting and reliable inference. Additionally we highlight some identifiability issues associated with SSMs that fisheries scientists should be aware of and demonstrate how our modelling strategy surmounts these problems. Using the Bay of Fundy sea scallop fishery we show that our implementation yields more conservative advice than that of the reference model. The Canadian Journal of Statistics 47: 27–45; 2019 © 2018 Statistical Society of Canada     

Residual-based CUSUM of squares test for Poisson integer-valued GARCH models

This study considers the problem of testing for a parameter change in integer-valued time series models in which the conditional density of current observations is assumed to follow a Poisson distribution. As a test, we consider the CUSUM of the squares test based on the residuals from INGARCH models and find that the test converges weakly to the supremum of a Brownian bridge. A simulation study demonstrates its superiority to the residual and standardized residual-based CUSUM tests of Kang and Lee [Parameter change test for Poisson autoregressive models. Scand J Statist. 2014;41:1136–1152] and Lee and Lee [CUSUM tests for general nonlinear inter-valued GARCH models: comparison study. Ann Inst Stat Math. 2019;71:1033–1057.] as well as the CUSUM of squares test based on standardized residuals.     

Approximate prediction limit for weibull failure based on preliminary test estimator

In this paper we have considered type II censored sample from a two parameter Weibull distribution with the known scale parameter. Using the preliminary test estimator of the unknown shape parameter (3 proposed by Pandey (1983), the paper derives a method of finding the approximate prediction limit for the minimum or, more generally,the j^th smallest of a set of future observations from the Weibull or even extreme-value distribution     

An online estimation scheme for a Hull–White model with HMM-driven parameters

This paper considers the implementation of a mean-reverting interest rate model with Markov-modulated parameters. Hidden Markov model filtering techniques in Elliott (1994, Automatica, 30:1399–1408) and Elliott et al. (1995, Hidden Markov Models: Estimation and Control. Springer, New York) are employed to obtain optimal estimates of the model parameters via recursive filters of auxiliary quantities of the observation process. Algorithms are developed and implemented on a financial dataset of 30-day Canadian Treasury bill yields. We also provide standard errors for the model parameter estimates. Our analysis shows that within the dataset and period studied, a model with two regimes is sufficient to describe the interest rate dynamics on the basis of very small prediction errors and the Akaike information criterion.     

当代国家和哲学之结构、关系和互动述论

当代国家和哲学之间关系的深层次结合不是某一个方面的单向度展开，而是从社会存在到社会意识，渐进地表现为结构一机制一规律一价值一行为五个层级与环节。国家摆脱不了哲学的指导，哲学也离不开这种或那种国家去实现。如果只在绝对中统一或只在抽象中统一，就沦为国家之外在哲学，唯物史观实现了具体的历史的统一，是国家之内在哲学，整个社会就是在国家和哲学的良性互动中实现其向前发展。     

基于DSP技术的钻井参数数据采集系统的设计

设计了一种基于DSP技术的钻井工程参数随钻数据采集系统。钻压、扭矩、侧向力、环空压力等钻井工程参数可以随钻进行测试。该数据采集系统采用TI公司的TMS320LF2407A芯片为主控制器,另外设计了相应的外围信号调理电路、存储模块、通信模块等。一个测试实例说明该数据采集系统用于井下钻井工程参数的测试是可行的。     

基层维稳新探索——天津唐口街“群众诉求调处四级网格”结构功能分析

新形势下的社会冲突大多数源起于基层。亟需我们稳定基础、防治兼顾。天津市唐口街创新社会管理机制建立群众诉求调处四级网格的做法,为疏导和化解基层冲突纠纷提供了有益启示。     

Testing goodness of fit via nonparametric function estimation techniques

An overview is given of methodology for testing goodness of fit of parametric models using nonparametric function estimation techniques. The ideas are illustrated in two settings: the classical one-sample goodness-of-fit scenario and testing the goodness of fit of a polynomial regression model.     

Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data

Summary.  A common objective in longitudinal studies is the joint modelling of a longitudinal response with a time-to-event outcome. Random effects are typically used in the joint modelling framework to explain the interrelationships between these two processes. However, estimation in the presence of random effects involves intractable integrals requiring numerical integration. We propose a new computational approach for fitting such models that is based on the Laplace method for integrals that makes the consideration of high dimensional random-effects structures feasible. Contrary to the standard Laplace approximation, our method requires much fewer repeated measurements per individual to produce reliable results.