Testing for Neglected Nonlinearity in Cointegrating Relationships*

Abstract. This article proposes pure significance tests for the absence of nonlinearity in cointegrating relationships. No assumption of the functional form of the nonlinearity is made. It is envisaged that the application of such tests could form the first step towards specifying a nonlinear cointegrating relationship for empirical modelling. The asymptotic and small sample properties of our tests are investigated, where special attention is paid to the role of nuisance parameters and a potential resolution using the bootstrap.     

基于Bootstrap法的连续压实指标目标值计算方法

有效控制填筑体压实质量是保障道路、大坝、机场等基础设施使用寿命和安全运营的关键。随着连续压实控制技术的广泛应用，科学合理地确定连续压实指标目标值对于填筑体质量控制具有重要意义。提出了一种基于Bootstrap法的连续压实指标目标值计算方法，与基于有限个试验数据采用经验公式获取压实指标目标值的传统方法相比，该方法从统计学角度，根据常规质量验收指标与连续压实指标的经验公式建立了置信度为(1-α)×100%的回归模型置信带，并通过选取置信上限来确定连续压实指标目标值。最后，结合国内外文献中相关地基压实工程中的现场试验数据对该方法的实用性进行验证，结果表明：该方法计算得到的连续压实指标目标值要优于常规经验公式计算值，且具有更高的可信度。该方法能够更合理地对由小样本不确定性导致的压实质量风险进行补偿，且补偿值随试验数据相关性的减小而增加，从而使压实质量控制标准更加科学与可靠。此外，相比于已有文献给出的置信区间，该方法所得置信区间宽度更窄，避免了过盈估计，具有较好的工程指导意义和应用前景。     

Optimal scheduling of electrical power in energy-deficient scenarios using artificial neural network and Bootstrap aggregating

In a developing country like Pakistan, where the electrical power demand is more than the generated power, maintaining the power system stability is a big challenge. In such cases it becomes, thus, essential to shed just the right amount of load to keep a power system stable. This paper presents a case study of Pakistan’s power system where the generated power, the load demand, frequency deviation and the load shed during a 24-h duration have been provided. The data have been analyzed using two techniques; the conventional artificial neural network (ANN) by implementing feed forward back propagation model and the Bootstrap aggregating or bagging algorithm. The simulation results reveal the superiority of the Bootstrap aggregating algorithm over a conventional ANN technique using feed forward back propagation model.     

Weighted bootstrap for neural model selection

This article proposes a weighted bootstrap procedure, which is an efficient bootstrap technique for neural model selection. Our primary interest in reducing computer effort is to not resample (in the original bootstrap procedure) uniformly from the original sample, but to modify this distribution in order to obtain variance reduction. The performance of the weighted bootstrap is demonstrated on two artificial data sets and one real dataset. Experimental results show that the weighted bootstrap procedure permits an approximately 2 to 1 reduction in replication size.     

Bootstrap-assisted Goodness-of-fit Tests in the Frequency Domain

The validity of a stationary time series model may be measured by the goodness of fit of the spectral distribution function. Anderson (Technical Report 27, 1991; Technical Report 309, 1995; Stanford University) has worked out the closed-form characteristic functions for the Cramer–von Mises criterion for general linear processes, under the condition that all values of the parameters are specified. The asymptotic approach is not easily implemented and usually requires a case by case analysis. In this paper we propose a bootstrap goodness-of-fit test in the frequency domain. By properly resampling the residuals, we can consistently estimate the p values for many weakly dependent semiparametric models with unspecified parameter values. This is the content of the main theorem that we try to explain. A group of simulations is conducted, showing consistent significance level and good power. The special tests are applied to the lynx data and reveal structure unexplained by the AR(1) model fitted by Tong ( J. R. Stat. Soc. A 140 (1977), 432–36). A possible generalization with application to financial data analysis is also discussed.     

Accuracy bounds and optimal computation of robot localization

We present an optimal method for estimating the current location of a mobile robot by matching an image of the scene taken by the robot with a model of the known environment. We first derive a theoretical accuracy bound and then give a computational scheme that can attain that bound, which can be viewed as describing the probability distribution of the current location. Using real images, we demonstrate that our method is superior to the naive least-squares method. We also confirm the theoretical predictions of our theory by applying the bootstrap procedure. Received: 17 March 1998 / Accepted: 7 April 2001     

An empirical model and scenario analysis of nitrous oxide emissions from a fertilised and grazed grassland site in Ireland

This paper describes an empirical model of soil-evolved nitrous oxide emissions inferred from data gathered in a 2-year rotational grazing experiment investigating emissions from a fertilised and grazed grassland site. The model was used to simulate daily and annual emissions for the 9-year period between 1994 and 2002 under different possible fertiliser application scenarios. As the model only requires a small number of commonly available site-specific data inputs, this facilitates its use at different geographical locations. This is the first empirical modelling of daily fluxes to give estimates of annual emissions. Results reveal a high interannual variability of emissions that increases with the amount of nitrogen fertiliser applied. Simulation using the model for the period 1994–2002 indicated that higher soil moisture status on the day of fertiliser N application increased the average annual nitrous oxide emissions.     

Testing for Effects on Variance in Experiments With Factorial Treatment Structure and Nested Errors

There is increased interest in industrial experiments aimed at improving quality by reducing the variability of a product characteristic while maintaining the desired mean level of the characteristic. Analysis of treatment effects on variability, however, is more difficult than analyzing the effects on mean performance. In this article we extend the results of Zelen to test for treatment effects on variance when there are two sources of variability, that which exists between production runs or setups and variability within runs. Bootstrap critical values are developed to handle possibly nonnormal errors at either level of variability.     

A HYBRID BOOTSTRAP APPROACH TO UNIT ROOT TESTS

This article proposes a hybrid bootstrap approach to approximate the augmented Dickey–Fuller test by perturbing both the residual sequence and the minimand of the objective function. Since innovations can be dependent, this allows the inclusion of conditional heteroscedasticity models. The new bootstrap method is also applied to least absolute deviation‐based unit root test statistics, which are efficient in handling heavy‐tailed time‐series data. The asymptotic distributions of resulting bootstrap tests are presented, and Monte Carlo studies demonstrate the usefulness of the proposed tests.     

Bootstrap resampling approach to disaggregate analysis of road crashes in Hong Kong

Road safety affects health and development worldwide; thus, it is essential to examine the factors that influence crashes and injuries. As the relationships between crashes, crash severity, and possible risk factors can vary depending on the type of collision, we attempt to develop separate prediction models for different crash types (i.e., single- versus multi-vehicle crashes and slight injury versus killed and serious injury crashes). Taking advantage of the availability of crash and traffic data disaggregated by time and space, it is possible to identify the factors that may contribute to crash risks in Hong Kong, including traffic flow, road design, and weather conditions. To remove the effects of excess zeros on prediction performance in a highly disaggregated crash prediction model, a bootstrap resampling method is applied. The results indicate that more accurate and reliable parameter estimates, with reduced standard errors, can be obtained with the use of a bootstrap resampling method. Results revealed that factors including rainfall, geometric design, traffic control, and temporal variations all determined the crash risk and crash severity. This helps to shed light on the development of remedial engineering and traffic management and control measures.