Testing the Granger Noncausality Hypothesis in Stationary Nonlinear Models of Unknown Functional Form

In this article, we propose a general method for testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. These tests are based on a Taylor expansion of the nonlinear model around a given point in the sample space. We study the performance of our tests by a Monte Carlo experiment and compare these to the most widely used linear test. Our tests appear to be well-sized and have reasonably good power properties.     

Implementing Monte Carlo tests with p-value buckets

Software packages usually report the results of statistical tests using p-values. Users often interpret these values by comparing them with standard thresholds, for example, 0.1, 1, and 5%, which is sometimes reinforced by a star rating (***, **, and *, respectively). We consider an arbitrary statistical test whose p-value p is not available explicitly, but can be approximated by Monte Carlo samples, for example, by bootstrap or permutation tests. The standard implementation of such tests usually draws a fixed number of samples to approximate p. However, the probability that the exact and the approximated p-value lie on different sides of a threshold (the resampling risk) can be high, particularly for p-values close to a threshold. We present a method to overcome this. We consider a finite set of user-specified intervals that cover [0, 1] and that can be overlapping. We call these p-value buckets. We present algorithms that, with arbitrarily high probability, return a p-value bucket containing p. We prove that for both a bounded resampling risk and a finite runtime, overlapping buckets need to be employed, and that our methods both bound the resampling risk and guarantee a finite runtime for such overlapping buckets. To interpret decisions with overlapping buckets, we propose an extension of the star rating system. We demonstrate that our methods are suitable for use in standard software, including for low p-value thresholds occurring in multiple testing settings, and that they can be computationally more efficient than standard implementations.     

食品检测咀嚼机器人工作空间研究

为了提高食品检测效率,改善食品检测精度,增加食品检测的客观性,文章介绍了一种基于6-UPS机构的咀嚼机器人,介绍了咀嚼机器人的结构原理,求解了机构的运动学逆解,分析了机构的工作空间影响因素,利用MATLAB编程求得机构在定姿态下的工作空间。空间轮廓图表明,该机构在定姿态下能到达人类全部真实咀嚼空间,机构设计可靠,能够模拟人类咀嚼行为对食品进行检测。     

基于灰色系统的小型精密机床热误差模型

为减少热误差对数控机床加工精度的影响,文章利用灰色系统研究热误差建模。初步分析机床热源后,应用灰 色关联度分析结合模糊分类方法优化得到13组关键温度测点,结合小型精密五轴机床处于实际加工状态时的实验数 据,应用GM(I,N)灰色结构进行建模。将实际数据与拟合数据进行对比分析,结果表明此灰色系统模型具有计算方法 成熟、鲁棒性强的特点,适用于机床热误差研究。     

立体标签自动贴标机结构设计

为了减少在产品包装环节中的人员参与,提高包装环节的自动化程度,研制了一种用于立体标签的自动贴标机。 根据产品贴标的流程,采用Pro/E软件进行三维建模和零部件的优化设计,包括传送机构、标签推进机构和折弯机构。 折弯机构中采用了折弯翻板实现对标签的折弯控制;自动贴标机各机构均采用了独立的电机控制,控制系统采用PLC 技术和变频器调速控制,设计了电动机正反转的控制电路图。通过对机械结构和电气控制系统优化设计,得到了满足设 计要求的自动贴标机机构设计参数,实现了对厚度为0.5 mm纸质标签纸的2次折弯并实现贴标过程。该自动标签机的 使用实现了包装过程的自动化,提高产品的包装效率。     

A Rejection Principle for Sequential Tests of Multiple Hypotheses Controlling Familywise Error Rates

We present a unifying approach to multiple testing procedures for sequential (or streaming) data by giving sufficient conditions for a sequential multiple testing procedure to control the familywise error rate (FWER). Together, we call these conditions a ‘rejection principle for sequential tests’, which we then apply to some existing sequential multiple testing procedures to give simplified understanding of their FWER control. Next, the principle is applied to derive two new sequential multiple testing procedures with provable FWER control, one for testing hypotheses in order and another for closed testing. Examples of these new procedures are given by applying them to a chromosome aberration data set and finding the maximum safe dose of a treatment.     

An F-type test for detecting departure from monotonicity in a functional linear model

When studying associations between a functional covariate and scalar response using a functional linear model (FLM), scientific knowledge may indicate possible monotonicity of the unknown parameter curve. In this context, we propose an F-type test of monotonicity, based on a full versus reduced nested model structure, where the reduced model with monotonically constrained parameter curve is nested within an unconstrained FLM. For estimation under the unconstrained FLM, we consider two approaches: penalised least-squares and linear mixed model effects estimation. We use a smooth then monotonise approach to estimate the reduced model, within the null space of monotone parameter curves. A bootstrap procedure is used to simulate the null distribution of the test statistic. We present a simulation study of the power of the proposed test, and illustrate the test using data from a head and neck cancer study.     

Classical testing in functional linear models

We extend four tests common in classical regression – Wald, score, likelihood ratio and F tests – to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications.     

食品安全检测紧缺人才培养模式的实践与探索

以新时期高等教育理念为指导,围绕产、学、研、用四个环节,教学实践中探索出食品安全人才培养模式:紧密结合生产、生活实际,注重实践应用;建设完善食品安全相关特色课程体系,提高教学水平,培养实用人才;结合食品安全法从法律角度提升人才培养,使培养的人才同时具备科学性和法律的规范性;研究性教学、研究性实验,由培养实用人才迈向培养有研究能力、创新能力的人才。