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RESEARCH HIGHLIGHTSMolecular methods are currently the most sensitive for detecting Chlamydia psittaci in birds. Most laboratories have developed their own molecular assays or adapted published protocols, often making slight modifications to fit their specific study purposes. The sensitivity and specificity of a molecular test depend on the target gene, primer sequences, types of molecular test, DNA extraction method, and sampling methods. We reviewed 120 articles published between 2000 and 2020 to compile information on the molecular detection of C. psittaci in birds. Of the ten genomic targets currently available to detect C. psittaci in birds, the ompA gene was the most widely used. In published surveillance studies, of the fourteen molecular test types, conventional PCR and quantitative PCR were applied the most. A testing strategy using a hierarchical approach that includes molecular tests of genus- and species-specific targets is recommended to detect other avian chlamydial species besides the well-recognized C. psittaci. Samples should be sourced from both the respiratory and gastrointestinal tracts whenever possible for better accuracy. High-quality DNA can be obtained when the sample is preserved in optimal medium and temperature, and an optimized DNA extraction protocol is applied. Standardization and validation of molecular Chlamydia tests are needed to enhance the comparability and reliability of assays to detect C. psittaci and other chlamydiae species in birds.Hierarchical molecular testing is recommended for the detection of avian C. psittaci.Key molecular tests for surveillance were conventional PCR and quantitative PCR.The most used genomic target to detect C. psittaci in birds was the ompA gene.Hierarchical molecular testing is recommended for the detection of avian C. psittaci.Key molecular tests for surveillance were conventional PCR and quantitative PCR.The most used genomic target to detect C. psittaci in birds was the ompA gene. [ABSTRACT FROM AUTHOR]

In recent years, the body of literature on automated vehicles (AV) regarding simulation, user acceptance, and accompanying research on pilot projects has increased substantially. However, studies investigating the relationship between user characteristics and preference of shared or pooled-use AV, such as automated taxis or automated shuttles, mainly investigate socioeconomic or mobility characteristics of potential users. Further, there are no studies on user-type sensitivity toward instruments fostering the uptake of shared and pooled-use AV. The aim of our study is to contribute to fill this gap, to provide important knowledge for decision-makers to develop transport policies designed to foster future sharing of AV. To this end, we re-analyze data from a choice experiment with 709 participants together with individual user characteristics, collected within the Swiss Household Energy Demand Survey. Specifically, we analyze the role of socioeconomic and mobility characteristics, attitudes and values regarding sensitivity toward policy-relevant push and pull instruments to foster shared and pooled-use AV mode choice. We show that different types of user characteristics have an impact on AV mode preference and on the sensitivity toward the categories and types of instruments tested. Furthermore, we show that it is worthwhile to differentiate between short-term mobility decisions on single trips and long-term mobility decisions, concerning purchase decisions for AV or subscriptions to AV-related mobility services. [ABSTRACT FROM AUTHOR]

Randomization procedures that enforce balance in prognostic factors, most commonly stratified randomization, are often employed in clinical trials. When the number of factors or factor levels is large, dynamic allocation procedures, such as the Pocock and Simon's covariate-adaptive randomization (minimization) are preferred. In their ground-breaking work Ye and Shao identified two classes of covariate-adaptive randomization procedures. They have demonstrated theoretically that for these classes, when the model is misspecified, the robust score test (Lin and Wei) as well as the unstratified log-rank test used for analysis of time-to-event endpoints, are valid or conservative (Ye and Shao). This fact, however, was not established for minimization other than through simulations of survival endpoints. In this article, we point out that the results of Ye and Shao can be expanded to a more general class of randomization procedures. We show, in part theoretically, in part through simulations of the within-strata imbalances, that minimization belongs to this class. Along the way we describe the asymptotic correlation matrix of the normalized within-stratum imbalances following minimization with equal prevalence of all strata. We expand the robust tests proposed by Ye and Shao for stratified randomization to minimization and examine their performance through simulations. [ABSTRACT FROM AUTHOR]

Hypothesis testing for the exponential distribution family consistently gains significant attentions from the statistical community, especially in situations characterized by small sample sizes. In this paper, we propose a novel approach for constructing a Kolmogorov-Smirnov type test based on representative points. This testing statistic is derived through a comparison between two empirical functions: one from the observed data and another from chosen representative points. A distribution-free quantile estimator is used for sample standardization. We also discuss how to select an optimal number of representative points based on the loss function, which is recommended for constructing the empirical function from the exponential distribution family. To evaluate the efficiency of this new test, a comprehensive Monte Carlo simulation is conducted to compare this new test with tailor-made tests. Simulation results show that our test statistic will be more powerful especially for small sample sizes, and it is competitive in the comparison with tailor-made tests. Some datasets are analyzed to further illustrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

In comparing the accuracy of any two competing forecasts, under the assumption that the loss differentials are covariance stationary, Diebold and Mariano (DM) proposed a test statistic that is asymptotically normal. This test is further studied by Giacomini and White (GW). However, under the DM-GW framework, the estimator of the variance of the DM test can be inaccurate in small samples, which yields size distortions; see, for example, Coroneo and Iacone (CI). To alleviate this challenge, we propose a maximum-subsampling (MS) test that does not require estimating the long-run variance induced by a number of autocovariances. Accordingly, the MS test is applicable for comparing the predictive ability between two competing forecasts even when the loss differentials are serially correlated with arbitrary autocovariance structures. We demonstrate that the MS test converges to the type I extreme value distribution under the DM-GW null hypothesis with proper conditions. In addition, under a set of alternative hypotheses, we show the MS test is consistent. We further compare the MS test with the DM test and two CI tests via five simulation settings, which are either modified or adapted from McCracken and Coroneo and Iacone. Simulation results show that the MS test performs satisfactorily. [ABSTRACT FROM AUTHOR]

AbstractHypothesis testing for the Weibull distribution always raises attention in the literature. It is challenging especially in small-sample scenarios. In this paper, we propose a new Kolmogorov-Smirnov type test based on the distance between two empirical functions, one from the data and another from the representative points of the underlying distribution. A bias correction technique is used to estimate unknown parameters in the Weibull distribution. We also discuss how to choose a suitable number of representative points based on the proposed loss function, which is recommended for constructing the empirical function from the Weibull distribution. To compare with tailor-made tests in the literature, a simulation study of the empirical type I error and testing power is conducted. It shows that our proposed test statistic is more powerful in most alternative scenarios, and it significantly improves the power of testing in small-sample scenarios. Finally, two real-world datasets are conducted to further demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

The filament-wound (FW) cylinders possess numerous benefits as a high-pressure storage device but are susceptible to impact loads. The presence of post-impact residual strength in an operating FW cylinder has the potential to accelerate the onset of failure. Ensuring the structural integrity and safety of the equipment requires thorough impact testing, which can be expensive and time-consuming, resulting in limited use. This article presents an innovative and cost-effective approach for conducting low-velocity impact testing on a hybrid Al/CFRP filament-wound cylinder by utilizing a simple hybrid Al/CFRP plate specimen. The main focus is to conduct low-velocity impact testing on both types of specimens, followed by post-damage testing that includes tensile tests on the hybrid plate specimen and burst tests on the FW cylinder. The results of laboratory experiments were compared to explicit and implicit numerical models conducted in a broad spectrum of impact energy range and found to be consistent. Using a parameter known as the post-impact residual strength ratio, it is possible to determine the expected impact loading conditions on the hybrid plate specimen that are equivalent to those experienced by the FW-cylinder. This low-cost impact testing method offers a practical and feasible solution to ensure the dependability and safety of FW cylinders throughout their operational lifespan. [ABSTRACT FROM AUTHOR]

This paper is concerned with investigating the adequacy of using the p-value as an estimator for the set specified by the null hypothesis in the Exponential distribution. It is shown that the p-value is an admissible estimator in the one-sided test of the location parameter. When the one-sided test of the scale parameter is considered, the p-value is found to be a generalized Bayes estimator with infinite Bayes risk. However, it is very difficult to find an estimator that dominates it. When the parameter space is restricted, the modified p-value is an admissible estimator in the one-sided test of the scale parameter and performs better than the usual p-value. Although the usual p-value is generally inadmissible in the two-sided test, it can be useful as an estimator in this type of test for the scale parameter. [ABSTRACT FROM AUTHOR]

We propose the Factor Augmented (sparse linear) Regression Model (FARM) that not only admits both the latent factor regression and sparse linear regression as special cases but also bridges dimension reduction and sparse regression together. We provide theoretical guarantees for the estimation of our model under the existence of sub-Gaussian and heavy-tailed noises (with bounded (1 + ϑ) th moment, for all ϑ > 0 ), respectively. In addition, the existing works on supervised learning often assume the latent factor regression or sparse linear regression is the true underlying model without justifying its adequacy. To fill in such an important gap on high-dimensional inference, we also leverage our model as the alternative model to test the sufficiency of the latent factor regression and the sparse linear regression models. To accomplish these goals, we propose the Factor-Adjusted deBiased Test (FabTest) and a two-stage ANOVA type test, respectively. We also conduct large-scale numerical experiments including both synthetic and FRED macroeconomics data to corroborate the theoretical properties of our methods. Numerical results illustrate the robustness and effectiveness of our model against latent factor regression and sparse linear regression models. for this article are available online. [ABSTRACT FROM AUTHOR]

We propose new classes of goodness-of-fit tests for the Pareto Type I distribution. These tests are based on a characterization of the Pareto distribution involving order statistics. We derive the limiting null distribution of the tests and also show that the tests are consistent against fixed alternatives. The finite-sample performance of the newly proposed tests are evaluated and compared to some of the existing tests, where it is found that the new tests are competitive in terms of powers. The paper concludes with an application to a real world data set, namely the earnings of the 22 highest paid participants in the inaugural season of LIV golf. [ABSTRACT FROM AUTHOR]

The increasing power demand growth and extreme weather conditions push utilities to search for a more sustainable and resilient power distribution infrastructure. Dispatchable distributed generators (DGs) can contribute to achieving the goals. This paper reveals a general framework for allocating dispatchable DGs with their suitable location, capacity, and type in the test distribution networks to bring sustainability and resilient planning synergy. A multiobjective optimization approach has been presented to minimize payback year (PBY) for DGs, enhance the network's operational efficiency, and promote system resiliency by satisfying various operating constraints. The proposed planning model is structured to give the best possible solution in compliance with changes in seasonal peak demands. The efficacy of the proposed scheme has been tested on a 28-bus Indian distribution system and an IEEE 33-bus test system, and results concerning the number of customers served, and priority customers restored are presented. [ABSTRACT FROM AUTHOR]

This study examines interpoint distance-based tests for random elements from probability distributions in infinite-dimensional space. Some asymptotic properties such as the limiting distributions and the asymptotic power of the Biswas–Ghosh type test (Biswas and Ghosh in J Multivar Anal 123: 160–171, 2014) are presented using the theory of U-statistic. In addition, the p-value approximation based on the jackknife variance estimators and the Welch–Satterthwaite equation is proposed. Simulation studies are conducted to evaluate the performance of the proposed test statistics in functional data analysis. The proposed tests are shown to have better power than the existing method for functional data in some situations. [ABSTRACT FROM AUTHOR]

We develop tests for predictability that are robust to both the magnitude of the initial condition and the degree of persistence of the predictor. While the popular Bonferroni Q test of Campbell and Yogo displays excellent power properties for strongly persistent predictors with an asymptotically negligible initial condition, it can suffer from severe size distortions and power losses when either the initial condition is asymptotically non-negligible or the predictor is weakly persistent. The Bonferroni t test of Elliott, and Stock, although displaying power well below that of the Bonferroni Q test for strongly persistent predictors with an asymptotically negligible initial condition, displays superior size control and power when the initial condition is asymptotically nonnegligible. In the case where the predictor is weakly persistent, a conventional regression t test comparing to standard normal quantiles is known to be asymptotically optimal under Gaussianity. Based on these properties, we propose two asymptotically size controlled hybrid tests that are functions of the Bonferroni Q, Bonferroni t, and conventional t tests. Our proposed hybrid tests exhibit very good power regardless of the magnitude of the initial condition or the persistence degree of the predictor. An empirical application to the data originally analyzed by Campbell and Yogo shows our new hybrid tests are much more likely to find evidence of predictability than the Bonferroni Q test when the initial condition of the predictor is estimated to be large in magnitude. [ABSTRACT FROM AUTHOR]

Systemic risk measures such as CoVaR, CoES, and MES are widely-used in finance, macroeconomics and by regulatory bodies. Despite their importance, we show that they fail to be elicitable and identifiable. This renders forecast comparison and validation, commonly summarized as "backtesting," impossible. The novel notion of multi-objective elicitability solves this problem by relying on bivariate scores equipped with the lexicographic order. Based on this concept, we propose Diebold–Mariano type tests with suitable bivariate scores to compare systemic risk forecasts. We illustrate the test decisions by an easy-to-apply traffic-light approach. Finally, we apply our traffic-light approach to DAX 30 and S&P 500 returns, and infer some recommendations for regulators. [ABSTRACT FROM AUTHOR]

Research suggests that people prefer human over algorithmic decision-makers at work. Most of these studies, however, use hypothetical scenarios and it is unclear whether such results replicate in more realistic contexts. We conducted two between-subjects studies (N=270; N=183) in which the decision-maker (human vs. algorithmic, Study 1 and 2), explanations regarding the decision- process (yes vs. no, Study 1 and 2), and the type of selection test (requiring human vs. mechanical skills for evaluation, Study 2) were manipulated. While Study 1 was based on a hypothetical scenario, participants in pre-registered Study 2 volunteered to participate in a qualifying session for an attractively remunerated product test, thus competing for real incentives. In both studies, participants in the human condition reported higher levels of trust and acceptance. Providing explanations also positively influenced trust, acceptance, and perceived transparency in Study 1, while it did not exert any effect in Study 2. Type of the selection test affected fairness ratings, with higher ratings for tests requiring human vs. mechanical skills for evaluation. Results show that algorithmic decision-making in personnel selection can negatively impact trust and acceptance both in studies with hypothetical scenarios as well as studies with real incentives. [ABSTRACT FROM AUTHOR]

A Wald type test with the wrong dispersion matrix is used when the dispersion matrix is not a consistent estimator of the asymptotic covariance matrix of the test statistic. One class of such tests occurs when there are p groups and it is assumed that the population covariance matrices from the p groups are equal, but the common covariance matrix assumption does not hold. The pooled t test, one-way ANOVA F test, and one-way MANOVA F test are examples of this class. Another class of such tests is used for weighted least squares. Two bootstrap confidence regions are modified to obtain large sample Wald type tests with the wrong dispersion matrix. [ABSTRACT FROM AUTHOR]

This article studies the projection test for high-dimensional mean vectors via optimal projection. The idea of projection test is to project high-dimensional data onto a space of low dimension such that traditional methods can be applied. We first propose a new estimation for the optimal projection direction by solving a constrained and regularized quadratic programming. Then two tests are constructed using the estimated optimal projection direction. The first one is based on a data-splitting procedure, which achieves an exact t-test under normality assumption. To mitigate the power loss due to data-splitting, we further propose an online framework, which iteratively updates the estimation of projection direction when new observations arrive. We show that this online-style projection test asymptotically converges to the standard normal distribution. Various simulation studies as well as a real data example show that the proposed online-style projection test retains the Type I error rate well and is more powerful than other existing tests. for this article are available online. [ABSTRACT FROM AUTHOR]

Melissopalynological knowledge, regarding the uniflorality of honeys, in terms of pollen content, depends on the plant source, but, in many cases, it is difficult to establish the threshold of pollen percentage for a honey to be considered unifloral. This research aims to increase knowledge for the classification of unifloral honeys through pollen analysis. The use of fresh experimental beehives placed under controlled conditions in 11 different specific areas of Euphorbia resinifera (resin spurge) enabled the production of the purest possible honey samples, which could then serve as a reference for testing uniflorality of this type of honey. The experimentally obtained results of the pollen analysis of these pure honey samples revealed a low absolute number of pollen grains and a low percentage of pollen of E. resinifera, which confirms the under-representation of the pollen of E. resinifera in honey labelled as this type. The pollen analysis enabled a combination of pollen grains to be established which greatly facilitates the differentiation of this honey type. Furthermore, we propose that the threshold that delineates the percentage of pollen necessary for this honey type to be considered unifloral can be lower than 10% if combined with a high percentage of the sum of Ceratonia siliqua/Eryngium f./Capsella f./Lactuca f. pollen. Using this approach, the uniflorality was tested of ten honey samples sold commercially as resin spurge using the aforementioned criteria. [ABSTRACT FROM AUTHOR]

Several tests for simultaneously checking homogeneity of means and covariance matrices, based on the classical likelihood ratio and sum-of-squares type test statistics, have been proposed in the literature. Despite their usefulness, they tend to have unsatisfactory size performance for either nonnormal high-dimensional data or strongly spiked eigenvalue models. This article proposes a novel high-dimensional nonparametric test using the maximum-type statistics. The limiting null distribution of the proposed test statistic is derived to provide p-values. It turns out that under appropriate conditions, the test is particularly powerful against sparse alternatives. Since the unknown correlations among the data sometimes pose a great challenge toward the accurate p-value calculation of the test, we further use a parametric bootstrap technique to achieve size accuracy. We also prove that the proposed simulation-based testing procedure is asymptotically valid in terms of size and power even if the dimension of the data is much larger than the sample size. We demonstrate the effectiveness of our methods through extensive simulation studies and a real data application. [ABSTRACT FROM AUTHOR]

Negligible diplomatic efforts have been advanced by the West to constructively engage with North Korea. For some time now, North Korea is testing various types of missiles on a regular basis and the possibility of accidental firing physically impacting South Korea and Japan is likely. This will increase the tensions in the region. On 21 November 2023, North Korea successfully launched a spy satellite that indicated the possible future trajectory of a space agenda. It is unlikely that North Korea would be ready for any deliberations on issues related to the nuclear and missile domain, at least in the near future. Under such a scenario, the optimal option is a process of constructive engagement. This article argues that the space domain offers such a possibility and reasons that for such engagement, the group of Brazil-Russia-India-China-South Africa (BRICS) is the best path forward. [ABSTRACT FROM AUTHOR]

The growing use of ultra-thin layers, called AUTL (Asphalt Ultra-Thin Layer), has raised the question of whether the currently used characterisation tests are equally applicable to mixes that are spread in such small thicknesses (10–20 mm). Among these tests are the shear tests mainly used for evaluating adhesion between layers. The guillotine type clamps, used in the tests such as the Leutner or any of its adaptations, can present problems when performing the test with thicknesses under 20 mm, so many regulations require minimum layer thicknesses. The aim of this study is to determine whether this type of test is still valid when one of the layers is thin or whether there are other tests that can be used in this case. For this, five different test types are considered: the usual shear test, a tensile test, an adaptation of the Fenix test, a torsion test and a fatigue test. This paper presents a description of these tests, as well as the results obtained with each of them after testing the cores extracted from a test section. The results demonstrate that all the tests can be valid and are capable of discriminating between the different tack coat dosages. [ABSTRACT FROM AUTHOR]

This paper analyzes police and public reactions to an intergroup-communication intervention developed and tested in Santa Barbara, California. We discuss the theory underlying the development of the intervention as well as its implementation as a means of improving relationships between police officers and members of the Hispanic community. Based on thematic analysis of interviews with program participants, we provide qualitative evidence about police and public reactions to the intervention. The data presented herein indicate that the intervention improved empathy and trust between participants from both groups. These results suggest that interventions based on principles of intergroup communication could be a useful mechanism for improving relationships between police and historically marginalized communities. They also provide a promising framework for research on how police can translate research into practice in the area of community trust-building with groups reluctant to engage with police. Future research should begin testing the effectiveness of these types of interventions using experimental or quasi-experimental methods. [ABSTRACT FROM AUTHOR]

Testing whether two spatial point processes have the same spatial distribution is an important task that can be addressed from different perspectives. A Kolmogorov-Smirnov test with asymptotic calibration and a Cramer von Mises type test with bootstrap calibration have recently been developed to compare the first-order intensity of two observed patterns. Motivated by common practice in epidemiological studies, we introduce a regression test based on the relative risk function with two alternative bootstrap calibrations. This paper compares the performance of these nonparametric tests through both an intensive simulation study, and the application to wildfire and crime data. The three tests provide good calibrations of the null hypothesis for simulated Poisson and non-Poisson spatial point processes, but the Cramer von Mises and regression tests outperform the cost-efficient Kolmogorov-Smirnov test in terms of power. In the real data analysis we have seen that the Kolmogorov-Smirnov test does not detect differences between spatial point patterns when dealing with sparse data. In view of these results, it would be preferable using the Cramer von Mises or regression tests despite their higher computational demand. [ABSTRACT FROM AUTHOR]

Adhesive bonding is a highly efficient joining technology for the repair of fibre-reinforced plastics. However, in civil aviation, its application is currently limited to non-critical joints. In safety critical cases, certification authorities demand proof of sufficient bond strength for each individual joint. Today, there is no practicable inspection method that can provide this kind of evidence. Recently, a novel method has been proposed that may resolve this issue. This so-called Bondline Control Technology (BCT) is a new type of adhesion test that provides proof of bond quality and simultaneously serves as a robust surface pretreatment for adhesive bonding. The present study investigates on the capability of the BCT to detect adhesion defects. This is done by means of in-lab tests on bonded specimens with adhesion defects of defined severity. The detection sensitivity of BCT coupons (Bondline Control Specimens, BCS) is compared with double-cantilever beam specimens (DCB specimens), which are known to be particularly capable of detecting adhesion defects. [ABSTRACT FROM AUTHOR]

We consider Kolmogorov–Smirnov and Cramér–von-Mises type tests for testing central symmetry, exchangeability, and independence. In the standard case, the tests are intended for the application to independent and identically distributed data with unknown distribution. The tests are available for multivariate data and bootstrap procedures are suitable to obtain critical values. We discuss the applicability of the tests to random effects models, where the random effects are independent but not necessarily identically distributed and with possibly unknown distributions. Theoretical results show the adequacy of the tests in this situation. The quality of the tests in models with random effects is investigated by simulations. Empirical results obtained confirm the theoretical findings. A real data example illustrates the application. [ABSTRACT FROM AUTHOR]

In model checking for regressions, nonparametric estimation-based tests usually have tractable limiting null distributions and are sensitive to oscillating alternative models, but suffer from the curse of dimensionality. In contrast, empirical process-based tests can, at the fastest possible rate, detect local alternatives distinct from the null model, yet are less sensitive to oscillating alternatives and rely on Monte Carlo approximation for critical value determination, which is costly in computation. We propose an adaptive-to-model hybrid of moment and conditional moment-based tests to fully inherit the merits of these two types of tests and avoid the shortcomings. Further, such a hybrid makes nonparametric estimation-based tests, under the alternatives, also share the merits of existing empirical process-based tests. The methodology can be readily applied to other kinds of data and construction of other hybrids. As a by-product in sufficient dimension reduction field, a study on residual-related central mean subspace and central subspace for model adaptation is devoted to showing when alternative models can be indicated and when cannot. Numerical studies are conducted to verify the powerfulness of the proposed test. [ABSTRACT FROM AUTHOR]

This study investigated the derivatives of interlock knitted structures by changing stitch type, location, and their percentage (%) on different performance properties. In the current study, 11 different interlock knit structures were developed by using multiple combinations of knit, tuck, and miss stitches and different types of testing, i.e., physical, mechanical, thermo-physiological comfort, tactile comfort, and dimensional properties, were performed and investigated. Analysis of variance (ANOVA) was performed to determine the statistical significance of structure on the properties. Based on the results, it was concluded that stitch type, percentage, and location have a prominent influence on the abovementioned properties of knit fabrics, which is also elaborated by interaction plots. The present research used a multi-response optimization technique, i.e., principal component analysis (PCA), to identify the best structure for industrial applications like uniforms, based on the optimization of abovementioned properties. Based on the results, it was suggested that a sample with an interlock cross relief structure was found to be the best in all aspects among all samples, followed by interlock pique and double tuck, which were ranked as 2nd and 3rd, respectively. This paper highlights the possible application of different interlock knitted structures according to end use for manufacturers. [ABSTRACT FROM AUTHOR]

Mundlak shows that the fixed effects estimator is equivalent to the random effects estimator in the one-way error component model once the random individual effects are modeled as a linear function of all the averaged regressors over time. In the spirit of Mundlak, this paper shows that this result also holds for the two-way error component model once the individual and time effects are modeled as linear functions of all the averaged regressors across time and across individuals. Wooldridge also shows that the two-way fixed effects estimator can be obtained as a pooled OLS with the regressors augmented by the time and individual averages and calls it the two-way Mundlak estimator. While Mundlak used GLS rather than OLS on this augmented regression, we show that both estimators are equivalent for this augmented regression. This extends Baltagi's results from the one-way to the two-way error component model. The F test suggested by Mundlak to test for this correlation between the random effects and the regressors generate a Hausman type test that is easily generalizable to the two-way Mundlak regression. In fact, the resulting F-tests for the two-way error component regression are related to the Hausman type tests proposed by Kang for the two-way error component model. [ABSTRACT FROM AUTHOR]

The traditional reliability inference based on the metric of mean life between failures for validating suppliers imposes insufficient deliberations on their supplied variability of unit-to-unit failure times as well as incompetent formation as a standardized metric to verify each supplier's products reliability. To fulfill this gap in this paper, we develop a standardized lifetime-capability metric based reliability hypothesis in the production verification test, wherein the amount of the mean life between failures outlives the stipulated warranty period is evaluated by the unit-to-unit lifetime deviation. This lifetime-capability based supplier qualification allows the distinct life scales yielded from life-testing applications being standardized into lifetime-capability scores, whereby the accessible reliability–capability tables for suppliers' validations can be easily built and enacted in practice. Furthermore, with physically accelerated Weibull-life type II testing data collected from supplier submissions, we advance statistical methodologies of the lifetime-capability metric for acquiring its lower confidence bound at a specified level of confidence to reveal the true lifetime-capability performance of the supplier. Moreover, since the lifetime-capability metric is capable of translating the supplier's reliability capability into financial perspectives of warranty returns and vice versa, this advancement enables frontline managers to select preferred suppliers according to their acceptable warranty return rate, thereby preventing non-economic warranty costs; on the other hand, with the buyer's warranty reserve set beforehand, the maximum allowable warranty return rate for the reserve and the minimum required lifetime-capability performance for the products are obtained to select the qualified suppliers. [ABSTRACT FROM AUTHOR]

Initially saturated, cementitious materials are subject to water movements that are responsible for multiple phenomena. Among them, there are drying shrinkage, creep, fluid transport, swelling reactions and cracking-induced changes in mechanical properties. The main objective of this study is therefore to investigate the impact of drying on the behaviour of cementitious materials. For this we carried out two 70-day studies on the material EDF's VeRCoRs project. The studies consist of storing (after almost complete hydration) a batch of specimen under saturated conditions while the second batch is stored in controlled environments of 30% and 45% hr, respectively. After the drying phase, specimens are tested by the mean of standard mechanical tests. The conclusions are heterogeneous. For instance, on different specimens that are not in water balance, the compressive strength is increased by a maximum of 12% while the tensile strength determined by bending is more dependent on the type of tests. The elastic modulus decreases with desaturation. Finally, the energy dissipated by the cracking increases sharply with drying. These results show that the hygro-mechanical behaviour is complex, depending on the ambient conditions and its duration, the geometry of the specimens and the type of tests. Thus, to take into account the hydric effects on mechanical behaviour, it is necessary to have a numerical modelling approach to take into account the different mechanisms involved. In this perspective, this work provides a significant database that offers the opportunity to test hygro-mechanical models. A necessary step in order to model structures. [ABSTRACT FROM AUTHOR]

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A pipe condition assessment device (PCAD) was developed to detect, characterise leakage and assess the condition of water distribution networks (WDN). In this study, the reliability and repeatability of the device results were established by first characterising known leak types on pipe sections using a standardised method. Then, three manufactured leak types were tested, with each leak tested ten times. These tests were then repeated using the PCAD and showed that the PCAD could produce results comparable to laboratory-acquired results within a 95% confidence interval. Field tests conducted in a real network revealed that the PCAD could detect and identify leakage characteristics in water networks. [ABSTRACT FROM AUTHOR]

In this article we derive the exact null distribution of a class of Hollander-Proschan type statistics for testing exponentiality against NWBUE alternatives. The exact null distribution of a previously established statistic is obtained as a special case. Critical values are tabulated for different combinations of parameter values. Performance of the tests for small sample sizes has also been studied in various alternative scenarios. We analyse two wellknown data sets in the light of our result. [ABSTRACT FROM AUTHOR]

The production effect is the superior memory for items read aloud as opposed to silently at the time of study. The distinctiveness account holds that produced items benefit from the encoding of additional elements associated with the act of production. If so, then that benefit should be consistent regardless of item type. Three experiments, using three different sets of materials and three different methods, tested this hypothesis. Experiment 1, using recognition testing, showed consistent production benefits for high and low frequency words. Experiment 2, using free recall, showed consistent production increments for pictures and words. Experiment 3, using incidental learning, showed consistent production benefits for recognition of nonwords and words. Taken together, these results fit with the distinctiveness account: Production at encoding dependably adds information to the memory record, regardless of item type or method of testing, producing a consistently reliable memory benefit. [ABSTRACT FROM AUTHOR]

With the introduction of large commercial industrial laboratories at the end of the nineteenth century, many types of experiments were institutionalized that do not aim at testing hypotheses. This paper builds a typology of experiments in techno-science, by analysing more than two hundred and fifty real-life technical projects. This resulted in four testing types (tests of hypotheses, of designs, of means-end knowledge, and of models or software), three determining types (developing working principles, preferred actions, and determining values of variables or relationships between variables) and one trial-and-error type of pure exploration. The typology is developed by working back and forth between thick descriptions of the experiments including their goals, and the development of six criteria of differentiation, to wit: determining versus testing; measurement scales of (in)dependent variables; intrinsic versus instrumental value of the outcomes; proximate function of the outcome; distant role of the outcome in the embedded project; the descriptive or normative character of the proximate or distant outcomes. The typology opens up inspiring methodological and philosophical research questions. [ABSTRACT FROM AUTHOR]

This article introduces the idea of self-instrumenting endogenous regressors in settings when the correlation between these regressors and the errors can be derived and used to bias-correct the moment conditions. The resulting bias-corrected moment conditions are less likely to be subject to the weak instrument problem and can be used on their own or in conjunction with other available moment conditions to obtain more efficient estimators. This approach can be applied to estimation of a variety of models such as spatial and dynamic panel data models. This article focuses on the latter, and proposes a new estimator for short T dynamic panels by augmenting Anderson and Hsiao (AAH) estimator with bias-corrected quadratic moment conditions in first differences which substantially improve the small sample performance of the AH estimator without sacrificing the generality of its underlying assumptions regarding the fixed effects, initial values, and heteroskedasticity of error terms. Using Monte-Carlo experiments it is shown that AAH estimator represents a substantial improvement over the AH estimator and more importantly it performs well even when compared to Arellano and Bond and Blundell and Bond (BB) estimators that are based on more restrictive assumptions, and continues to have satisfactory performance in cases where the standard GMM estimators are inconsistent. Finally, to decide between AAH and BB estimators we also propose a Hausman type test which is shown to work well when T is small and n sufficiently large. [ABSTRACT FROM AUTHOR]

This paper is concerned with testing and dating structural breaks in the dependence structure of multivariate time series. We consider a cumulative sum (CUSUM) type test for constant copula-based dependence measures, such as Spearman's rank correlation and quantile dependencies. The asymptotic null distribution is not known in closed form and critical values are estimated by an i.i.d. bootstrap procedure. We analyze size and power properties in a simulation study under different dependence measure settings, such as skewed and fat-tailed distributions. To date breakpoints and to decide whether two estimated break locations belong to the same break event, we propose a pivot confidence interval procedure. Finally, we apply the test to the historical data of 10 large financial firms during the last financial crisis from 2002 to mid-2013. [ABSTRACT FROM AUTHOR]

This article introduces the idea of self-instrumenting endogenous regressors in settings when the correlation between these regressors and the errors can be derived and used to bias-correct the moment conditions. The resulting bias-corrected moment conditions are less likely to be subject to the weak instrument problem and can be used on their own or in conjunction with other available moment conditions to obtain more efficient estimators. This approach can be applied to estimation of a variety of models such as spatial and dynamic panel data models. This article focuses on the latter, and proposes a new estimator for short T dynamic panels by augmenting Anderson and Hsiao (AAH) estimator with bias-corrected quadratic moment conditions in first differences which substantially improve the small sample performance of the AH estimator without sacrificing the generality of its underlying assumptions regarding the fixed effects, initial values, and heteroskedasticity of error terms. Using Monte-Carlo experiments it is shown that AAH estimator represents a substantial improvement over the AH estimator and more importantly it performs well even when compared to Arellano and Bond and Blundell and Bond (BB) estimators that are based on more restrictive assumptions, and continues to have satisfactory performance in cases where the standard GMM estimators are inconsistent. Finally, to decide between AAH and BB estimators we also propose a Hausman type test which is shown to work well when T is small and n sufficiently large. [ABSTRACT FROM AUTHOR]

We explored the impact a new quantitative biology course for biology majors had on their ability and confidence to utilize hypothesis testing. Students currently in the quantitative biology course and the first cohort of students who took the course (currently seniors) were surveyed on ability and confidence regarding four types of hypothesis testing. Overall student confidence increased for when and why to perform t-tests following the course, and improvement with t-tests and analysis of variance continued for seniors. Incorporation of a quantitative biology course into the curriculum for biology majors provides students with the opportunity to apply statistical methods to real-world biological situations. [ABSTRACT FROM AUTHOR]

Chi-square type test statistics are widely used in assessing the goodness-of-fit of a theoretical model. The exact distributions of such statistics can be quite different from the nominal chi-square distribution due to violation of conditions encountered with real data. In such instances, the bootstrap or Monte Carlo methodology might be used to approximate the distribution of the statistic. However, the sample quantile may be a poor estimate of the population counterpart when either the sample size is small or the number of different values of the replicated statistic is limited. Using statistical learning, this article develops a method that yields more accurate quantiles for chi-square type test statistics. Formulas for smoothing the quantiles of chi-square type statistics are obtained. Combined with the bootstrap methodology, the smoothed quantiles are further used to conduct equivalence testing in mean and covariance structure analysis. Two real data examples illustrate the applications of the developed formulas in quantifying the size of model misspecification under equivalence testing. The idea developed in the article can also be used to develop formulas for smoothing the quantiles of other types of test statistics or parameter estimates. [ABSTRACT FROM AUTHOR]

Within the European FP7 Project "INSYSME", the research unit of the University of Pavia has developed a seismic-resistant masonry infill system with original details, which subdivides the masonry panel into horizontal stripes through the insertion of "sliding joints" and deformable joints at the infill–frame interface. The out-of-plane stability is governed by the flexural/arching resistance of the masonry stripes and adequate supports at the RC columns. An extensive experimental campaign has been performed. In this paper, the results of the out-of-plane dynamic tests on shaking-table conducted on the specimens with and without opening are discussed. These types of tests, never applied before on innovative masonry infill typologies, have involved a very demanding testing protocol, constituted of artificial input motions at increasing intensity. To interpret the test results and provide first indications for safety checks, simplified numerical and analytical simulations of the tests have been conducted. According to the out-of-plane experimental performance of this innovative infill system and to the satisfactory in-plane response, the solution appears to be very promising for real applications. [ABSTRACT FROM AUTHOR]

Parametric models for analysis of survival data with possible crossing of hazard rates related with two treatment groups are introduced. Strategy for survival improvement through application of time-varying treatment is discussed. Complete and right-censored data with possible confounding covariates are considered. Estimators of the crossing points are given. Chi-square type goodness-of-fit tests for the considered models are given. Parametric tests for the absence of crossing of survival functions (and also for crossing of the hazard functions) hypothesis are proposed. [ABSTRACT FROM AUTHOR]

The classical two-sample t-test is not robust to skewed populations, and the large sample approximation has low accuracy for finite sample sizes. This paper presents two new types of tests, the TCFU and the TT tests, for comparing populations with unequal variances. The TCFU test uses Welch's t-statistic as the test statistic and the Cornish–Fisher expansion as its critical values. The TT tests apply transformations to Welch's t-statistic and use the normal percentiles as critical values. Four monotone transformations are considered for the TT tests. We give asymptotic expansions for the power functions of the new tests accurate to the order of O (n − 1). A comparison of different tests in terms of power and type I error is presented both theoretically and through Monte Carlo experiments. Analytical conditions are derived to help practitioners choose a powerful test. [ABSTRACT FROM AUTHOR]

We translate the simplicial data depth concept of Liu (1990) into a bivariate test of location that is distribution free under the assumption of angular symmetry about zero, without loss of generality. The test statistic is a count of the number of data triangles that contain zero and rejects the null hypothesis for small values of the test statistic. A straightforward method for computing this statistic is provided. The exact null distribution is computed and tabled for n-15, the asymptotic null distribution is derived and a formula for approximate critical values is provided. Simulations show that the power of the test is comparable to other bivariate sign type tests. The analogous one dimensional distribution free test is found to be equivalent to a two sided one sample sign test. These ideas are then extended to three dimensions, where we show that, surprisingly, the test is not generally distribution free under the null hypothesis. [ABSTRACT FROM AUTHOR]

The determination of uniaxial compressive strength (UCS) is central to most mining and geotechnical engineering analyses and designs in rock engineering. Direct measurement of UCS through uniaxial compression tests are costly and time-consuming. This is a challenge in mining engineering practice. However, there is a possibility to have results of other tests (e.g. punch tests) which are simple to conduct, particularly at the early stage of many mining engineering projects. The information contained in the results of multiple punch tests can be combined under a Bayesian approach to characterise UCS. This study proposes a Bayesian approach for probabilistic characterisation of UCS based on information from multiple sources, including the results of multiple punch tests available at a specific rock site. The proposed Bayesian approach is formulated to sequentially incorporate data from the parameters of three punch tests, namely Schmidt rebound hardness (SRH), block punch index (BPI) and point load strength ( I s (50) ) to update statistics and probability distribution of UCS. The approach is illustrated using real SRH, BPI and data at a sandstone site. The proposed Bayesian approach is shown to perform satisfactorily for the probabilistic characterisation of UCS as results of additional type of punch tests are incorporated. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop a player type determination scale based on Bartle's taxonomy. In this study, player type determination tests prepared for Bartle's player types were translated into Turkish by two English experts. The Turkish version of the items was revised by two field experts to best express the point of each item. Next, a scale consisting of 50 items was formed in accordance with the purpose of the study and the level of secondary school students. The scale was administered to 505 secondary school students, and validity and reliability studies were conducted. As a result of a factor analysis, it was decided to remove some items from the test, leading to a measurement instrument of 32 items in total. It was concluded that the scale developed to determine the player type for digital gaming environments was valid and reliable. [ABSTRACT FROM AUTHOR]

Recent research conducted as part of the English Profile identified grammatical criterial features that are characteristic of each Common European Framework of Reference (CEFR) proficiency level. The extent to which these criterial features attest in English learners of various first language backgrounds call for an empirical examination. In this study, I investigated the use of the criterial features drawing upon a Korean EFL corpus. Data consisted of 6,486 essays produced by 3,243 Korean college students from A1 to B2 CEFR proficiency levels. I examined how 35 criterial features which pertain to levels A2, B1, and B2 attested in this corpus. Overall, although more frequent and wide use of the features was observed as proficiency level advanced, the features occurred in low frequencies. I also found some misalignments between the criterial level of the features and learner proficiency. I discuss the potential influence of first language, test and task type, and rating on the findings of this study. Moreover, I raise the need to reconsider the criteriality of the identified features depending on the context when adopting and applying them in local assessment contexts. [ABSTRACT FROM AUTHOR]

Testing for predictability of asset returns has been a long history in economics and finance. Recently, based on a simple predictive regression, Kostakis, Magdalinos, and Stamatogiannis derived a Wald type test based on the context of the extended instrumental variable (IVX) methodology for testing predictability of stock returns, and Demetrescu showed that the local power of the standard IVX-based test could be improved for some range of alternative hypotheses and the tuning parameter when a lagged predicted variable is added to the predictive regression on purpose, which poses an important question on whether the predictive model should include a lagged predicted variable. This article proposes novel robust procedures for testing both the existence of a lagged predicted variable and the predictability of asset returns regardless of regressors being stationary or nearly integrated or unit root and the AR model for regressors with or without an intercept. A simulation study confirms the good finite sample performance of the proposed tests before illustrating their practical usefulness in analyzing real financial datasets. [ABSTRACT FROM AUTHOR]

The main aim FIgure 9of this work is to design, develop and experimentally test the performance of an improved box-type solar cooker with thermal energy storage. The improvement features are the ability to concentrate solar rays and store thermal energy. The improved solar cooker became 20% less in inner surface area compared to the conventional solar cooker for the same intercept area of the sun. Experiments are conducted for stagnation test and load test followed by comparison of the thermal performance of improved solar cooker with conventional one. The performance testing parameters are used as per the Indian standard for box type solar cooker. Testing results showed that the averaged first figure of merits (F1) is 0.115 for conventional and, 0.1349 for improved solar cooker with black stone as a thermal energy storage, 0.1238 for improved solar cooker with concrete as a thermal energy storage and 0.1453 for improved solar cooker without any thermal storage. As per the Indian standard for testing of box type solar cooker, the improved box-type solar cookers in all condition are found to be grade 'A' due to the fact that the first figure of merits (F1) is greater than 0.12. Also the thermal energy storage materials used gives a remarkable storing capacity. [ABSTRACT FROM AUTHOR]

Four decades of research have shown that students' low test-taking effort is a serious threat to the validity of score-based inferences from low-stakes, group-based educational assessments. This meta-analysis sought to identify effective interventions for improving students' test-taking effort in such contexts. Included studies (a) used a treatment-control group design; (b) administered a low-stakes group-based educational assessment; (c) employed an intervention to improve test-taking motivation; and (d) evaluated test-taking effort and/or test performance as outcomes. The analysis included 53 studies (N = 59,096) that produced 60 and 105 effect sizes of test-taking effort and test performance, respectively. On average, interventions were found to improve test-taking effort and test performance by 0.13 standard deviations (SD) each. The largest gains in test-taking effort were observed when providing external incentives followed by increasing test relevance, while no significant differences were found between these two intervention types in improving test performance. Furthermore, negligible impact was detected on both dependent variables for interventions that modified assessment design or promised feedback. Recommendations for future research and practice are discussed. [ABSTRACT FROM AUTHOR]