Proposed Test Procedure Research Articles

Investigation of the strain rate and stretch level dependent behavior of elastomeric nanocomposites in complex uniaxial tests under finite strains

The mechanical behavior of elastomeric nanocomposites in experiments with nested stress-strain cycles and cyclic tests with increasing strain amplitude were considered. In our proposed testing procedures, long time stops at each stage of the change in loading direction were of great importance. This revealed two significant features of the behavior of elastomeric nanocomposites that have received little attention. It was shown that material softening (the Mullins effect) should be considered not only in the elastic part of the Cauchy stress tensor, but also in its dissipative part. The second peculiarity was the difference between the characteristic relaxation time at loading and the characteristic relaxation time at unloading observed in the experiments.This paper focuses on the behavior of highly-filled elastomeric materials based on different matrices (styrene-butadiene rubber (SBR) and nitrile-butadiene rubber (NBR)) and with different concentrations of carbon black (CB) or a combination of two fillers (CB and purified multi-walled carbon nanotubes (MWCNTs)).A mathematical model of the viscoelastic behavior of elastomeric nanocomposites under finite strains was proposed. It takes into account the peculiarities of the behavior of highly filled elastomers observed in the experiments. The specificity of the model consists in a new variant of the form of the free energy potential. It was shown that the new model satisfies the thermodynamic inequality, which is a consequence of the first law of thermodynamics and the second law in the form of the Clausius-Duhem inequality. A good agreement between theoretical calculations and experimental data was obtained.

Parameter Estimation and Testing for the Doubly Geometric Process with Lognormal Distribution: Application to Bladder Cancer Patients’ Data

The geometric process (GP) has been widely utilized as a stochastic monotone model in the fields of probability and statistics. However, its practical application is often limited by certain assumptions. To address this, [Wu (2018). Doubly geometric process and applications. Journal of the Operational Research Society, 69(1), 66–67] introduced the doubly geometric process (DGP) as an extension of the GP model, relaxing some of its assumptions. Due to its ability to overcome the limitations of the GP model, the DGP has gained significant popularity in recent times. This study focuses on the parameter estimation problem for the DGP when the distribution of the first interarrival time follows a lognormal distribution with parameters [Formula: see text] and [Formula: see text]. We employ the maximum likelihood method to obtain estimates for both the model parameters and the distribution parameters. Additionally, we investigate the asymptotic joint distribution and statistical properties such as asymptotic unbiasedness and consistency of the estimators. Furthermore, we propose a novel test procedure to distinguish between the GP and DGP models. To assess the performance of the estimators and the proposed test procedure, we conduct a simulation study involving various sample sizes and parameter values. Finally, we present an application of the developed methods in fitting data from bladder cancer patients.

Tests under simple order in one‐way ANCOVA

AbstractAnalysis of covariance (ANCOVA) models are used when apart from the main treatments, some covariates also affect the response variable. In this article, the problem of testing the homogeneity of treatment effects against ordered alternatives is addressed in a fixed effects one‐way ANCOVA model when error variances are heterogeneous. The likelihood ratio test (LRT) and two approximate tests based on the asymptotic distributions of some union‐intersection type test statistics are proposed. A parametric bootstrap technique has been used to implement the LRT and its asymptotic validity is proved. A method to construct simultaneous confidence intervals is proposed. All the test procedures are further extended to the case of more than one covariate. The robustness of tests is also studied under departure from normality. Extensive simulation studies show that the proposed test procedures perform well in terms of achieving the nominal size value and good power values.

Improved Test Procedure and Sample Size Calculation for Assessing Similarity in Two-Group Comparative Studies with Heterogeneous Variances

The two one-sided tests (TOST) method for mean equivalence or average equivalence has been extended to assessing similarity or switchability for individual equivalence in clinical trials. Tolerance interval procedures are available to establish similarity with respect to the proportion of the response differences covered by a prespecified threshold range. However, the extended TOST procedures based on tolerance intervals are potentially susceptible to the control of Type I errors. This article aims to present an exact approach with the specified Type I error probability for appraising similarity between two treatments in comparative studies with heterogeneous variances. Analytic examination and numerical comparison are conducted to clarify the utility of the suggested similarity test and the drawback of the current TOST procedures. To enhance the usefulness of the described exact method, the related power and sample size issues are also considered. Computer algorithms are provided to implement the proposed test procedure, power calculation, and sample size determination in similarity studies.

Detecting Weak Distribution Shifts via Displacement Interpolation

Detecting weak, systematic distribution shifts and quantitatively modeling individual, heterogeneous responses to policies or incentives have found increasing empirical applications in social and economic sciences. Given two probability distributions P (null) and Q (alternative), we study the problem of detecting weak distribution shift deviating from the null P toward the alternative Q, where the level of deviation vanishes as a function of n, the sample size. We propose a model for weak distribution shifts via displacement interpolation between P and Q, drawing from the optimal transport theory. We study a hypothesis testing procedure based on the Wasserstein distance, derive sharp conditions under which detection is possible, and provide the exact characterization of the asymptotic Type I and Type II errors at the detection boundary using empirical processes. We demonstrate how the proposed testing procedure works in modeling and detecting weak distribution shifts in real datasets using two empirical examples: distribution shifts in consumer spending after COVID-19, and heterogeneity in the published p-values of statistical tests in journals across different disciplines.

A Generalized Residual-Based Test for Fractional Cointegration in Panel Data with Fixed Effects

Asymptotic theories for fractional cointegrations have been extensively studied in the context of time series data, with numerous empirical studies and tests having been developed. However, most previously developed testing procedures for fractional cointegration are primarily designed for time series data. This paper proposes a generalized residual-based test for fractionally cointegrated panels with fixed effects. The test’s development is based on a bivariate panel series with the regressor assumed to be fixed across cross-sectional units. The proposed test procedure accommodates any integration order between [0,1], and it is asymptotically normal under the null hypothesis. Monte Carlo experiments demonstrate that the test exhibits better size and power compared to a similar residual-based test across varying sample sizes.

A combined superiority and non-inferiority procedure for comparing predictive values of two diagnostic tests

Positive and negative predictive values are useful to quantify the performance of medical tests, and both are often used simultaneously. Although there are several methods to test the equality of these predictive values between two medical tests, these approaches separately compare positive and negative predictive values. Therefore, we propose a testing procedure that combines the approximate likelihood ratio test defined by Tang et al. with the non-inferiority test for predictive values. The procedure can confirm that compared to an existing test, a new medical test is non-inferior in terms of both positive and negative predictive values, as well as superior regarding at least one of these values. It can make a comprehensive judgment of the performance of the new test based on both measures. A simulation study showed that the performance of the proposed testing procedure is appropriate, and the procedure is considered useful for evaluating the performance of predictive values of medical tests.

Tests for homogeneity of distributions of component lifetimes from Type‐II censored system lifetime data with known system structures

AbstractIn system reliability analysis, researchers are interested in evaluating the characteristics of the lifetime distribution of components in a system based on system lifetime data. In collecting system lifetime data, a censoring scheme is often adopted due to time and budget constraints. In this article, we consider the situation where the system lifetime data from two different coherent systems are subjected to Type‐II right‐censoring, and we are interested in testing the homogeneity of component lifetime distributions based on Type‐II censored system lifetime data with known system structures. Based on the Mann‐Whitney test and empirical likelihood ratio tests developed for testing the homogeneity of component lifetime distributions with complete system lifetime data, we propose different non‐parametric test procedures using the permutation of the unobserved censored system lifetimes. We consider a restricted assumption on the censored lifetimes to reduce the permutations required in the computation. The computational approaches to obtain the critical values of the proposed test procedures are provided using the Monte Carlo method. A practical example is used to illustrate the proposed test procedures. Then, the power performance of the proposed test procedures is evaluated and compared through a Monte Carlo simulation study. The simulation results show that the proposed test procedures provide comparable power performance for Type‐II censored system lifetime data in contrast to the complete sample case under different scenarios. Finally, recommendations based on the simulation results and concluding remarks are provided.

Testing Spatial Dynamic Panel Data Models with Heterogeneous Spatial and Regression Coefficients

Spatio‐temporal data are often analysed by means of spatial dynamic panel data (SDPD) models. In the last decade, several versions of these models have been proposed, generally based on specific assumptions and estimator properties. We focus on an SDPD model with heterogeneous coefficients both in the spatial and exogeneous regression components. We propose a strategy to identify the specific structure of the SDPD model through a multiple testing procedure that allows to choose between a general version of the model and a nested version derived from the general one by imposing restrictions on the parameters. Our proposal can be used to test the homogeneity of the model parameters as well as the absence of specific components, such as spatial effects, dynamic effects or exogenous regressors. It is also possible to use the proposed testing procedure for the identification of relevant locations. The theoretical results highlight the consistency of the testing procedure in the high‐dimensional setup, when the number of spatial units goes to infinity and exceeds the number of time‐observations per spatial unit. Further, we also conduct a Monte Carlo simulation study, which gives empirical evidence of the good performance of the testing procedure in finite samples.

The Computational Testing Procedure for the Comprehensive Lifetime Performance Index of Burr XII Products in Multiple Production Lines

Extending from a single production line to multiple production lines, a comprehensive life performance index is proposed for evaluating the quality of lifetime products. The connection between the comprehensive lifetime performance index and the individual lifetime performance index is explored. For products with a lifetime following Burr XII distribution for the ith production line, the maximum likelihood estimation method and the corresponding asymptotic distribution for all lifetime performance indices are derived. Checking whether the comprehensive lifetime performance index has achieved the target value is essentially the same as testing whether each individual lifetime performance index has reached its corresponding target value. A testing procedure is proposed for a given significance level using the maximum likelihood estimator as the test statistic, and the power analysis is presented through graphical representations. For the power analysis, the impacts of sample size, the number of inspection intervals, the removal probability, the level of significance, and the number of production lines on the test power are analyzed, and the results show that there is a monotonic relationship between the test power and the above five impact factors. To illustrate how to apply the proposed testing procedure, we give one practical example with two production lines to test whether the comprehensive production process is capable.

Test procedure for the evaluation of partially automated driving HMI including driver monitoring systems in driving simulation

The proposed test procedure presents an approach for the evaluation of the usability of partial automated driving HMI including driver monitoring systems in driving simulation. This procedure is based on a definition of requirements that a Level 2 HMI and its included driver monitoring system must fulfill in order to guarantee that the drivers understand their responsibilities of continuously monitoring the driving environment and the status of the partial automated driving system. These requirements are used to define the evaluation criteria that have to be validated in the test as well as the use cases in which these criteria can be assessed. The result is a detailed and comprehensive test guide including the specification of the test drives, the necessary instructions, the test environment and the recruiting criteria for the test sample.•Evaluation of usability aspects of level 2 automated driving HMI including driver monitoring systems•Based on the definition of requirements for L2 HMI•Test guide including the definition of use cases, evaluation criteria and testing conditions in driving simulation

Comparison of paired ordinal data with mis-classification and covariates adjustment

Abstract In this paper, we develop an estimation and testing procedure for comparing matched-pair ordinal outcomes in studies with confounding factors. The classification method for the categories of ordinal outcomes that is accessible for all units may be prone to mis-classification, and thus another error-free classification method that can only be affordable for a fraction of the units are used, resulting in a dataset with partial validation. The distribution of categorical variables is modelled using correlated bivariate Gaussian latent variables, and the confounding factors are adjusted as covariates. The mis-classification of ordinal outcomes is addressed by estimating the mis-classification probabilities through the partial validation structure of the dataset. The mis-classification probabilities and the other parameters are estimated by a two-stage maximum likelihood estimator, and the difference between the matched-pair ordinal outcomes are assessed by a Wald test statistic. Simulation studies were conducted to investigate the accuracy of the estimates of the model parameters, and the type I error rates and power of the proposed testing procedure. The motivating dataset from the Garki Project was analysed to demonstrate the applicability of the proposed approach.

Optimization of Realistic Accelerated Stress Tests for PEM Fuel Cells Using Standardized Automotive Driving Cycles

Proton exchange membrane (PEM) fuel cells have so far not achieved the targeted lifetime necessary to be an economical alternative for automotive applications. This is mainly caused by their high degradation rates. To better understand the degradation mechanisms, the use of accelerated stress tests (ASTs) at the component level is widespread. However, it is still a challenge to transfer their results to real-world applications.To address this problem, the approach of this work is to derive load profiles from standardized driving cycles for passenger cars. In combination with a fuel cell system model that is used to define the operating conditions of the media, such as temperature or pressure, a test procedure is obtained that results in a realistic degradation of the fuel cell. The proposed test procedure exposes the PEM fuel cell (PEMFC) to the most important degradation conditions in mobile applications. Dynamic operation is responsible for deterioration processes within the PEMFC and is the underlying cause of typical hygrothermal and voltage cycling events. Such cycling also occurs during start-stop operations, causing severe damage to the PEMFC catalysts. In addition, idling conditions may prompt open-circuit voltage (OCV) conditions in the PEMFC, resulting in dissolution of the Pt catalyst and subsequent chemical degradation of the PEM. Additionally, high load operation may also lead to catalyst degradation. Finally, the inertia of the auxiliary components, such as the humidifier, during load changes causes not ideal conditions, such as relative humidity changes, resulting in further mechanical stress on the membrane.Due to the targeted PEMFC lifetime of up to 8,000 hours for automotive applications [1], realistic degradation test procedures have a very long test duration, making them particularly challenging and costly to conduct. To overcome this hurdle, the load profile of the proposed test procedure is shortened by using an in-house developed optimization algorithm. For this purpose, four operation modes are defined as degradation-accelerating: voltage cycling, relative humidity (RH) cycling, OCV/idling, and high load operation. Based on state-of-the-art AST procedures, a sub-algorithm is developed for each operating mode. Such sub-algorithms are capable of identifying all occurrences within a given test procedure that may lead to accelerated fuel cell degradation. For each sub-algorithm, the degradation parameters (namely upper and lower potential limits for voltage cycling, membrane tensile stress for RH cycling, fluoride emission rate for OCV/idling, and voltage degradation rate for high load) are specified by the user. This allows the comparison of different fuel cells in terms of their resistance to conventional degradation mechanisms.The developed algorithm can be used to optimize various driving cycles such as the worldwide harmonized light vehicles test cycle (WLTC) for various mobile applications. All cycle events identified as accelerating degradation are connected with a point connector function based on the fuel cell polarization curve. This ensures moderate voltage steps between the identified phases and thus prevents the algorithm from inducing further degradation of the fuel cell.The optimization algorithm obtained can reduce the test duration by up to 40%. The method can be repeated frequently during AST operation by repeatedly measuring the polarization curve to consider the continuous degradation in the test procedure. Decreasing fuel cell performance leads to more severe voltage drops during load changes and thus an increase of degradation-accelerating phases over the test period. Furthermore, start-stop ASTs developed by Bisello et al. [2] are also included due to their strong contribution to fuel cell degradation in automotive applications.Finally, the optimized cycles are analyzed using two simulation tools to evaluate the degradation. The first tool by Ao et al. [3] uses the electrochemical surface area (ECSA) to quantify the proceeding degradation during cycle operation. A characteristic voltage and the voltage change rate are extracted from the driving cycle. For both values, the resulting decrease in ECSA is modeled and aging is defined as the ECSA loss rate. The second tool by Pei et al. [4] examines the driving cycle according to the four degradation-inducing conditions and combines them with experimentally determined degradation rates. Both tools allow an analysis of the degradation derivative between the initial and optimized test procedures. By adjusting the degradation rate for the different degradation mechanisms, the AST procedure is improved in terms of its representativeness for real-world applications.

Testing Heteroskedasticity in High-Dimensional Linear Regression

A new procedure that is based on the residuals of the Lasso is proposed for testing heteroskedasticity in high-dimensional linear regression, where the number of covariates can be larger than the sample size. The theoretical analysis demonstrates that the test statistic exhibits asymptotic normality under the null hypothesis of homoskedasticity, and the simulation results reveal that the proposed testing procedure obtains accurate empirical sizes and powers. Finally, the procedure is applied to real economic data.

Increasing the efficiency of li-ion battery cycle life testing with a partial-machine learning based end of life prediction

Extensive cycle life studies are conducted in the laboratory to understand the aging behavior of a li-ion battery under various environmental and operational conditions. However, designing an optimal test plan is not straightforward and usually leads to test plans with test points providing little information gain. This paper proposes a machine learning-enhanced testing procedure, which stops the expensive cycling of a subset of tested batteries and replaces their physical test result with a machine learning-based prediction. The core component of the proposed testing procedure is a test subset selection algorithm, which selects a subset of batteries during the cycling whose aging can be predicted with low errors. This is done by extracting aging features during the initial cycling period and feeding them into the Kennard-Stone algorithm to select batteries that have a similar distribution of their aging features as batteries that are physically cycled. Based on three different aging studies and four different machine learning-based predictors, it is shown that the proposed selection approach helps to reliably find subsets that can be predicted with significantly lower prediction errors than random selections. A feature set with capacity-, internal resistance-, and incremental capacity-based features is proposed, which helps to select battery subsets with low prediction errors across all three tested aging studies. Furthermore, it is shown that a higher dissimilarity between a battery and the batteries within the training set also results in a higher prediction error for the respective battery. Therefore, a dissimilarity-based threshold is defined to effectively select the most batteries for the prediction. For the three investigated aging studies, the proposed ML-enhanced testing approach allows to stop the cycling of 30% to 75% of all batteries while ensuring EOL predictions with < 10% MAPE.

VALID HETEROSKEDASTICITY ROBUST TESTING

Tests based on heteroskedasticity robust standard errors are an important technique in econometric practice. Choosing the right critical value, however, is not simple at all: conventional critical values based on asymptotics often lead to severe size distortions, and so do existing adjustments including the bootstrap. To avoid these issues, we suggest to use smallest size-controlling critical values, the generic existence of which we prove in this article for the commonly used test statistics. Furthermore, sufficient and often also necessary conditions for their existence are given that are easy to check. Granted their existence, these critical values are the canonical choice: larger critical values result in unnecessary power loss, whereas smaller critical values lead to overrejections under the null hypothesis, make spurious discoveries more likely, and thus are invalid. We suggest algorithms to numerically determine the proposed critical values and provide implementations in accompanying software. Finally, we numerically study the behavior of the proposed testing procedures, including their power properties.

EM-test for homogeneity in location-scale mixture model with a structural scale

The testing problem for the homogeneity of a location-scale mixture model is fundamental for subsequent statistical inference. In this paper, we employ the EM-test method to test the homogeneity of two commonly encountered location-scale mixture models with a structural scale, and we also establish the theoretical properties of test statistics. Further, to enhance the reliability of the test, based on the computer experiments approach, we develop a Bartlett-corrected approximate distribution for the proposed test statistics. Through Monte Carlo studies and real data example, the effectiveness of the proposed testing procedure is compared with that of the classical one, especially for small or moderate sample sizes.

Reliability and Cost Assessment of Critical Components: Electrical generator failure of IDOM wave energy converter

It is believed that current testing procedures in the wave energy sector are not well-balanced. Most laboratory testing has been focused on functional tests (e.g. of proof of concept and performance assessment for example) disregarding other key performance measures such as reliability and survivability. A new testing procedure that reduces development time and cost, while enabling better understanding of reliability and survivability of critical components at early Technology Readiness Levels (TRLs) has been proposed within the VALID project.&#x0D; &#x0D; In the context of VALID, the work in this paper relates reliability of the critical component to the cost of the whole system, i.e. the wave energy converter. There is usually a trade-off between a very reliable component (low maintenance and high unit cost) against a component with a lower lifetime, lower unit cost and higher maintenance. The loss of revenue of a component failing more time during the project lifetime shall be evaluated against different maintenance strategies and the capital expenditures of the overall system and the component of focus. For that, the LCOE or Levelised Cost of Electricity seems to be the parameter more relevant to capture all these variations. A sensitivity analysis will consider how varying failure rates, component’s cost and maintenance costs affect CAPEX, OPEX and accumulated energy production along project’s lifetime.&#x0D; &#x0D; The assessment will focus on selected critical components representative of three wave energy technologies: CorPower, IDOM and Wavepiston. The three user cases of the VALID project will guide the work, though it is the aim that results will be applicable beyond the VALID project.&#x0D; Initially, a relative quantification of the impact on the LCOE of increasing the performance and reliability of the critical component will be done. As output data comes from the experimental results, this quantification will be refined.&#x0D; VALID is a Horizon 2020 research project where fourteen partners around Europe are collaborating into developing a new hybrid testing platform and methodology for critical components. VALID aims at integrating both reliability and testing methods together with relevant data on component failures early in the design and testing process to ensure that the proposed testing procedure is built upon past experience. The focus of the present paper is to statistically relate the realibility of the critical component to the costs of whole system.

A double-robust test for high-dimensional gene coexpression networks conditioning on clinical information.

It has been increasingly appealing to evaluate whether expression levels of two genes in a gene coexpression network are still dependent given samples' clinical information, in which the conditional independence test plays an essential role. For enhanced robustness regarding model assumptions, we propose a class of double-robust tests for evaluating the dependence of bivariate outcomes after controlling for known clinical information. Although the proposed test relies on the marginal density functions of bivariate outcomes given clinical information, the test remains valid as long as one of the density functions is correctly specified. Because of the closed-form variance formula, the proposed test procedure enjoys computational efficiency without requiring a resampling procedure or tuning parameters. We acknowledge the need to infer the conditional independence network with high-dimensional gene expressions, and further develop a procedure for multiple testing by controlling the false discovery rate. Numerical results show that our method accurately controls both the type-I error and false discovery rate, and it provides certain levels of robustness regarding model misspecification. We apply the method to a gastric cancer study with gene expression data to understand the associations between genes belonging to the transforming growth factor β signaling pathway given cancer-stageinformation.

Artificial Neural Network-Based Nonlinear Black-Box Modeling of Synchronous Generators

This article deals with the black-box modeling of synchronous generators based on artificial neural networks (ANN). The ANN is applied to define the relationship between the excitation and terminal generator voltage values, and the Levenberg–Marquardt algorithm is used for determining the ANN weight coefficients. The relation is made based on generator response on reference voltage step changes. The proposed approach is checked using the experimental results obtained from the measurements on a real 120 MVA generator from a hydroelectric power plant Piva in Montenegro. Furthermore, a fair comparison of the nonlinear autoregression model with the exogenous input (NARX) and Hammerstein–Wiener model is made. For the validation, different experiments were conducted—different values of step disturbances, other controller parameters, and different rotating speeds. Based on the presented results, it can be noted that the proposed ANN model is very accurate and provides a very high degree of matching with the experimental results and outperforms the other considered nonlinear models. Furthermore, the proposed test procedure and model are easy to implement and do not require disconnection of the generator from the grid or additional equipment for experimental realization. Such obtained models can be used for different testing types related to the excitation system.