Efficient Testing Procedure Research Articles

On zero-inflated hyper-Poisson distribution and its applications

Here we consider a zero-inflated version of the hyper-Poisson distribution and study some of its important properties through deriving its probability generating function and expressions for factorial moments, mean, variance, recursion formulae for probabilities, raw moments, and factorial moments. The estimation of the parameters of the zero-inflated hyper-Poisson distribution is attempted. The distribution has been fitted to certain real life data sets and thereby shown that the proposed model gives better fit to the data sets compared to existing models such as zero-inflated Poisson distibution (ZIPD), zero-inflated negative binomial distribution (ZINBD), zero-inflated Conway-Maxwell Poisson distribution (ZICMPD), and zero-inflated generalized Poisson distribution (ZIGPD). Further, Rao’s efficient score test procedure is applied for examining the significance of the parameters and a simulation study is carried out for assessing the performance of the maximum likelihood estimators of the parameters of the distribution.

On the efficiency of racetrack betting market: a new test for the favourite-longshot bias

ABSTRACTA number of empirical studies on the efficiency of racetrack betting market have shown the ‘favourite-longshot bias,’ which means longshots are overbet while favourites are underbet. Asian markets such as Hong Kong and Japan, however, have produced some contradictory empirical evidence to the bias. One critical element in the efficiency test procedure is how to assess the unobservable objective winning probability of a horse in a race. This paper proposes a new test framework with a more general evaluation of the objective probability of winning than the traditional method. Unlike the traditional method, our model allows the heterogeneity of the horses and the races. We apply the new empirical method to test whether the favourite-longshot bias is present in racetrack betting market of Korea. We found that the favourite-longshot bias exists in the racetrack market of Korea and the result distinguishes Korean racetrack market from other Asian markets.

An optimal kernel-based U-statistic method for quantitative gene-set association analysis.

Single-variant-based genome-wide association studies have successfully detected many genetic variants that are associated with a number of complex traits. However, their power is limited due to weak marginal signals and ignoring potential complex interactions among genetic variants. The set-based strategy was proposed to provide a remedy where multiple genetic variants in a given set (e.g., gene or pathway) are jointly evaluated, so that the systematic effect of the set is considered. Among many, the kernel-based testing (KBT) framework is one of the most popular and powerful methods in set-based association studies. Given a set of candidate kernels, the method has been proposed to choose the one with the smallest p-value. Such a method, however, can yield inflated Type 1 error, especially when the number of variants in a set is large. Alternatively one can get p values by permutations which, however, could be very time-consuming. In this study, we proposed an efficient testing procedure that cannot only control Type 1 error rate but also have power close to the one obtained under the optimal kernel in the candidate kernel set, for quantitative trait association studies. Our method, a maximum kernel-based U-statistic method, is built upon the KBT framework and is based on asymptotic results under a high-dimensional setting. Hence it can efficiently deal with the case where the number of variants in a set is much larger than the sample size. Both simulation and real data analysis demonstrate the advantages of the method compared with its counterparts.

Selection of a performance test to assess the cracking resistance of asphalt concrete materials

Cracking is a common failure mechanism in asphalt concrete pavement structures. It is one of the main reasons for large road maintenance and rehabilitation expenditures, as well as reduced user comfort and increased fuel consumption due to high road roughness. The resistance of the pavement to this distress mechanism is dependent upon the ductility of the asphalt pavement mixture. The use of recycled asphalt materials in asphalt mixtures are also becoming increasingly common. A drawback of this practice is a reduction in ductility of the asphalt mixture, which causes a significant reduction in the fatigue life of the pavement in many cases. In Oregon, asphalt pavements are commonly failing prematurely due to cracking-related distresses, necessitating costly rehabilitation and maintenance at intervals of less than half of the intended design lives in some cases. For this reason, it is necessary to accurately quantify the impact of increasing the recycled asphalt content in asphalt pavement on the structural cracking resistance of the pavement through the use of low-cost and efficient testing procedures that can be implemented easily. This study focuses on characterizing the cracking performance of asphalt pavements in Oregon by considering four tests commonly used to evaluate fatigue cracking resistance and proposing the implementation of the most cost-effective and efficient test procedure for agencies and contractors.

Similarity-based prioritization of test case automation

The importance of efficient software testing procedures is driven by an ever increasing system complexity as well as global competition. In the particular case of manual test cases at the system integration level, where thousands of test cases may be executed before release, time must be well spent in order to test the system as completely and as efficiently as possible. Automating a subset of the manual test cases, i.e, translating the manual instructions to automatically executable code, is one way of decreasing the test effort. It is further common that test cases exhibit similarities, which can be exploited through reuse when automating a test suite. In this paper, we investigate the potential for reducing test effort by ordering the test cases before such automation, given that we can reuse already automated parts of test cases. In our analysis, we investigate several approaches for prioritization in a case study at a large Swedish vehicular manufacturer. The study analyzes the effects with respect to test effort, on four projects with a total of 3919 integration test cases constituting 35,180 test steps, written in natural language. The results show that for the four projects considered, the difference in expected manual effort between the best and the worst order found is on average 12 percentage points. The results also show that our proposed prioritization method is nearly as good as more resource demanding meta-heuristic approaches at a fraction of the computational time. Based on our results, we conclude that the order of automation is important when the set of test cases contain similar steps (instructions) that cannot be removed, but are possible to reuse. More precisely, the order is important with respect to how quickly the manual test execution effort decreases for a set of test cases that are being automated.

Adaptive False Discovery Rate Control for Heterogeneous Data

Efforts to develop more efficient multiple hypothesis testing procedures for false discovery rate (FDR) control have focused on incorporating an estimate of the proportion of true null hypotheses (such procedures are called adaptive) or exploiting heterogeneity across tests via some optimal weighting scheme. This paper combines these approaches using a weighted adaptive multiple decision function (WAMDF) framework. Optimal weights for a flexible random effects model are derived and a WAMDF that controls the FDR for arbitrary weighting schemes when test statistics are independent under the null hypotheses is given. Asymptotic and numerical assessment reveals that, under weak dependence, the proposed WAMDFs provide more efficient FDR control even if optimal weights are misspecified. The robustness and flexibility of the proposed methodology facilitates the development of more efficient, yet practical, FDR procedures for heterogeneous data. To illustrate, two different weighted adaptive FDR methods for heterogeneous sample sizes are developed and applied to data.

Integrated genomic analysis of biological gene sets with applications in lung cancer prognosis

BackgroundBurgeoning interest in integrative analyses has produced a rise in studies which incorporate data from multiple genomic platforms. Literature for conducting formal hypothesis testing on an integrative gene set level is considerably sparse. This paper is biologically motivated by our interest in the joint effects of epigenetic methylation loci and their associated mRNA gene expressions on lung cancer survival status.ResultsWe provide an efficient screening approach across multiplatform genomic data on the level of biologically related sets of genes, and our methods are applicable to various disease models regardless whether the underlying true model is known (iTEGS) or unknown (iNOTE). Our proposed testing procedure dominated two competing methods. Using our methods, we identified a total of 28 gene sets with significant joint epigenomic and transcriptomic effects on one-year lung cancer survival.ConclusionsWe propose efficient variance component-based testing procedures to facilitate the joint testing of multiplatform genomic data across an entire gene set. The testing procedure for the gene set is self-contained, and can easily be extended to include more or different genetic platforms. iTEGS and iNOTE implemented in R are freely available through the inote package at https://cran.r-project.org//.

Ordered Regressions

AbstractThere are often situations where two or more regression functions are ordered over a range of covariate values. In this paper, we develop efficient constrained estimation and testing procedures for such models. Specifically, necessary and sufficient conditions for ordering generalized linear regressions are given and shown to unify previous results obtained for simple linear regression, for polynomial regression and in the analysis of covariance models. We show that estimating the parameters of ordered linear regressions requires either quadratic programming or semi‐infinite programming, depending on the shape of the covariate space. A distance‐type test for order is proposed. Simulations demonstrate that the proposed methodology improves the mean square error and power compared with the usual, unconstrained, estimation and testing procedures. Improvements are often substantial. The methodology is extended to order generalized linear models where convex semi‐infinite programming plays a role. The methodology is motivated by, and applied to, a hearing loss study.

Fast and robust phase stability testing at isothermal-isochoric conditions

In this paper, a robust and efficient phase stability testing procedure at isotherm-isochoric conditions is proposed. The tangent plane distance function is expressed in terms of component molar densities. Convergence behavior using various sets of independent variables is analyzed and it is shown that the choice of independent variables is very important for both robustness and computational speed. A particular attention is paid for conditions in which the Hessian matrix is notoriously ill-conditioned, that is, in the vicinity of the stability test limit locus (STLL) and of the spinodal, where the convergence is slow and problematic. It is found that poor conditioning occurs also at certain conditions in the two-phase region at low temperatures and towards high densities. A modified Cholesky factorization (to ensure a descent direction) and a line search procedure are used in Newton iterations. The equation of state (EoS) must not be solved for volume and any pressure explicit EoS can be used; only expressions of pressure, fugacity and their partial derivatives with respect to mole numbers are required. The proposed method is tested on several examples from the literature, using a refined grid in the temperature-molar density plane. The variables obtained by scaling the ideal part of the Hessian matrix lead by far to the most robust formulation, with no failure observed for the investigated mixtures, and much faster than previous methods, with a low average number of iterations required for convergence for all mixtures.

Formal Hypothesis Tests for Additive Structure in Random Forests

ABSTRACTWhile statistical learning methods have proved powerful tools for predictive modeling, the black-box nature of the models they produce can severely limit their interpretability and the ability to conduct formal inference. However, the natural structure of ensemble learners like bagged trees and random forests has been shown to admit desirable asymptotic properties when base learners are built with proper subsamples. In this work, we demonstrate that by defining an appropriate grid structure on the covariate space, we may carry out formal hypothesis tests for both variable importance and underlying additive model structure. To our knowledge, these tests represent the first statistical tools for investigating the underlying regression structure in a context such as random forests. We develop notions of total and partial additivity and further demonstrate that testing can be carried out at no additional computational cost by estimating the variance within the process of constructing the ensemble. Furthermore, we propose a novel extension of these testing procedures using random projections to allow for computationally efficient testing procedures that retain high power even when the grid size is much larger than that of the training set.

An efficient and flexible test for rare variant effects.

Since it has been claimed that rare variants with extremely small allele frequency play a crucial role in complex traits, there is great demand for the development of a powerful test for detecting these variants. However, due to the extremely low frequencies of rare variants, common statistical testing methods do not work well, which has motivated recent extensive research on developing an efficient testing procedure for rare variant effects. Many studies have suggested effective testing procedures with reasonably high power under some presumed assumptions of parametric statistical models. However, if the parametric assumptions are violated, these tests are possibly under-powered. In this paper, we develop an optimal, powerful statistical test called the aggregated conditional score test (ACST) for simultaneously testing M rare variant effects without restrictive parametric assumptions. The proposed test uses a test statistic aggregating the conditional score statistics of effect sizes of M rare variants. In simulation studies, ACST generally performed well compared with the two most commonly used tests, the optimal sequence kernel association test (SKAT-O) and Kullback-Leibler distance test. Finally, we demonstrate the performance and practical utility of ACST using the Dallas Heart Study data.

A new efficient free-rolling tyre-testing procedure for the parameterisation of vehicle dynamics tyre models

This paper describes an efficient tyre test procedure that can be used to collect the data required to parameterise empirical tyre models used in the computer simulations of vehicle dynamics. The new GS2MF FreeRolling test procedure develops established methodologies, such as the time measurement procedure from the TIME project and the MICH2MF measurement protocol developed as alternatives to traditional square matrix testing. The new process is designed to reduce the number of expensive tyre tests without compromising the accuracy of the generated tyre model parameters. The process is demonstrated by a programme of tyre testing carried out using the Calspan TIRF tyre testing facility in the USA, and it is shown here how the GS2MF Flat-Trac® tyre test procedure can be used more efficiently to parameterise the pure lateral and self-aligning moment components for the well-known ‘magic formula’ tyre model. This is achieved using a ‘cruise’-type procedure which is more representative of conditions existing when driving a real vehicle. During the tests, a novel automated logic approach is also proposed to manage the temperature of the tyres. Finally, graph sweeps are introduced at the start and the end of the tests, allowing a judgement to be made as to the influence of the tyre wear on the data obtained throughout the tests. The development of accurate and representative tyre models remains a significant challenge as vehicle manufacturers target increased use of virtual prototypes and simulations. This work contributes to this challenge by improving the efficiency of the expensive testing process needed to parameterise the models.

Antibacterial Efficiency of Benzalkonium Chloride Base Disinfectant According To European Standard 13727, Chemical Analysis and Validation Studies

Antibacterial Efficiency of Benzalkonium Chloride Base Disinfectant According To European Standard 13727, Chemical Analysis and Validation Studies This study was aimed to provide principle of the chemical analyses, antibacterial efficiency test and validation procedures of the most commonly used benzalkonium chloride (BAC) base disinfectant as a biocide. Disinfectant which comprised 20 % BAC concentration was used as a prototype product and active substance was verified with chemical analysis. Additionally, BAC homologs ( n -C12 and n -C14) in the product were identified by LC-MS/MS method. The validation results also confirmed the reliability of the chromatographic method. After active substance was confirmed, microbiological efficacy test was carried out against E. coli K12, S. aureus , P. aerugionas and E. hirae . Both bacterial strains were showed more than 5 logaritmic reduction after exposure for 1 min. to test product. The aim of this study was to evaluate the validation of chemical analysis of the test product by LC-MS/MS which is developed chromatographic method. Moreover the present study highlights that the antibacterial efficacy of BAC based disinfectant according to European National (EN) Standard. These results exhibited that chemical and microbiological analyses were significantly important before manufacturing and marketing processes of the biocidal products. This was the first study on biocides analysis which was conducted according to the European standard.

Air damping influence on dynamic parameters of laminated composite plates

Damping performance of lightweight composite structures, like fibre reinforced plastics (FRPs) is more efficient than the conventional materials. This phenomena was found valuable in the aerospace and transport industries. For this reason accurate material properties of FRPs are required for precise prediction of dynamic response. However, the given properties are often burden with a significant uncertainty level due to environment influence which includes air damping, temperature changes, humidity, etc. The present study concerns with the air damping influence on the dynamic behaviour of laminated composites plates with moderate damping level. The effect on dynamic characteristics is evaluated experimentally using a vacuum chamber and a laser vibrometer. Resonant frequencies and modal loss factors changes are investigated in terms of material type, sample size and excitation amplitude. A semi-automatic and time efficient two-step testing procedure is developed for a precise measurement of loss factors of laminated composite plates.

Testing for high‐dimensional geometry in random graphs

AbstractWe study the problem of detecting the presence of an underlying high‐dimensional geometric structure in a random graph. Under the null hypothesis, the observed graph is a realization of an Erdős‐Rényi random graph G(n, p). Under the alternative, the graph is generated from the model, where each vertex corresponds to a latent independent random vector uniformly distributed on the sphere , and two vertices are connected if the corresponding latent vectors are close enough. In the dense regime (i.e., p is a constant), we propose a near‐optimal and computationally efficient testing procedure based on a new quantity which we call signed triangles. The proof of the detection lower bound is based on a new bound on the total variation distance between a Wishart matrix and an appropriately normalized GOE matrix. In the sparse regime, we make a conjecture for the optimal detection boundary. We conclude the paper with some preliminary steps on the problem of estimating the dimension in . © 2016 Wiley Periodicals, Inc. Random Struct. Alg., 49, 503–532, 2016

An urn model to construct an efficient test procedure for response adaptive designs

We study the statistical performance of different tests for comparing the mean effect of two treatments. Given a reference classical test $${\mathcal {T}}_0$$ , we determine which sample size and proportion allocation guarantee to a test $${\mathcal {T}}$$ , based on response-adaptive design, to be better than $${\mathcal {T}}_0$$ , in terms of (a) higher power and (b) fewer subjects assigned to the inferior treatment. The adoption of a response-adaptive design to implement the random allocation procedure is necessary to ensure that both (a) and (b) are satisfied. In particular, we propose to use a Modified Randomly Reinforced Urn design and we show how to perform the model parameters selection for the purpose of this paper. Then, the opportunity of relaxing some assumptions on treatment response distributions is presented. Results of simulation studies on the test performance are reported and a real case study is analyzed.

A Brief Ode to the Empirical Likelihood Concept

robust likelihood-type techniques. Towards this end, the modern biostatistical literature has shifted focus towards robust and efficient nonparametric likelihood methods [2]. The empirical likelihood (EL) methodology employs the likelihood concept in a distribution-free manner, approximating optimal parametric likelihood-based procedures. Since EL techniques and parametric likelihood methods are closely related concepts, one may apply corresponding EL functions to replace their parametric likelihood counterparts in known and well developed parametric procedures, e.g. constructing novel nonparametric Bayesian inference [3] and the confidence interval estimation [4]. This provides the impetus for an impressive expansion in the number of EL developments based on combinations of likelihoods of different types, e.g., when incomplete data should be analyzed, nonparametric likelihood ratio techniques can be combined with parametric likelihoods [5]. The classical EL methodology, which is a distribution functionbased approach, has been shown to have attractive properties for testing hypotheses regarding parameters (e.g. moments) of distributions [6]. In practice, statisticians commonly face a variety of distribution-free comparisons and/or evaluations over all distribution functions of complete and incomplete data subject to different types of measurement errors. In these frameworks, the density-based EL methodology is shown to be very efficient [710]. According to the Neyman-Pearson lemma, the most powerful test statistics have structures that are related to density-based likelihood ratios. The density-based EL method can be easily and satisfactorily applied to construct highly efficient test procedures, approximating non-parametrically most powerful NeymanPearson test-rules, given aims of clinical studies. Similarly to the parametric likelihood concept, the EL methodology provides relatively simple strategies to construct powerful statistical tests that can be applied in various complex biostatistical studies. The extreme generality of EL methods and their wide ranges of usefulness partly result from the simple derivation of the EL statistics as components of composite parametric and nonparametric likelihood based systems, efficiently attending to any observed data and relevant information. The EL based methods are employed in much of modern biostatistical practice.

Hupotest – Highly Efficient Mental Test and Training Procedure.

HuPoTest is the name of a procedure establishing human potentials driven by mental state discovered incidentally in 1967. This procedure belongs to the classical procedures of calibration of measuring instruments, so that HuPoTest actually calibrates the timer of the person under test (PUT). Timer and mentality are strongly interconnected defining each other. Simply said, a good mentality is based on good timer (stable and well tuned) and both of them define the vital potential driving a good health. PUT has to count periods of time of 5, 10, 15 and 20 seconds in special conditions, the measured values are retrieved statistically by a software and the final values stored in a data bank in view to reveal the evolution of the health state (vital potential) according to established significances. The main resulted parameters are given in the example table. HuPoTest is a powerful mental test and a training procedure as well in view to show and improve the life style and avoid any kind of irreversible conditions/diseases. Hu Po Test can not be tricked! I.E. the PUT can not appear as he is not in reality

Efficient Noninferiority Testing Procedures for Simultaneously Assessing Sensitivity and Specificity of Two Diagnostic Tests.

Sensitivity and specificity are often used to assess the performance of a diagnostic test with binary outcomes. Wald-type test statistics have been proposed for testing sensitivity and specificity individually. In the presence of a gold standard, simultaneous comparison between two diagnostic tests for noninferiority of sensitivity and specificity based on an asymptotic approach has been studied by Chen et al. (2003). However, the asymptotic approach may suffer from unsatisfactory type I error control as observed from many studies, especially in small to medium sample settings. In this paper, we compare three unconditional approaches for simultaneously testing sensitivity and specificity. They are approaches based on estimation, maximization, and a combination of estimation and maximization. Although the estimation approach does not guarantee type I error, it has satisfactory performance with regard to type I error control. The other two unconditional approaches are exact. The approach based on estimation and maximization is generally more powerful than the approach based on maximization.

Sample size calculation based on efficient unconditional tests for clinical trials with historical controls

ABSTRACTIn historical clinical trials, the sample size and the number of success in the control group are often considered as given. The traditional method for sample size calculation is based on an asymptotic approach developed by Makuch and Simon (1980). Exact unconditional approaches may be considered as alternative to control for the type I error rate where the asymptotic approach may fail to do so. We provide the sample size calculation using an efficient exact unconditional testing procedure based on estimation and maximization. The sample size using the exact unconditional approach based on estimation and maximization is generally smaller than those based on the other approaches.