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Multivariate (average) equivalence testing is widely used to assess whether the means of two conditions of interest are `equivalent' for different outcomes simultaneously. The multivariate Two One-Sided Tests (TOST) procedure is typically used in this context by checking if, outcome by outcome, the marginal $100(1-2\alpha$)\% confidence intervals for the difference in means between the two conditions of interest lie within pre-defined lower and upper equivalence limits. This procedure, known to be conservative in the univariate case, leads to a rapid power loss when the number of outcomes increases, especially when one or more outcome variances are relatively large. In this work, we propose a finite-sample adjustment for this procedure, the multivariate $\alpha$-TOST, that consists in a correction of $\alpha$, the significance level, taking the (arbitrary) dependence between the outcomes of interest into account and making it uniformly more powerful than the conventional multivariate TOST. We present an iterative algorithm allowing to efficiently define $\alpha^{\star}$, the corrected significance level, a task that proves challenging in the multivariate setting due to the inter-relationship between $\alpha^{\star}$ and the sets of values belonging to the null hypothesis space and defining the test size. We study the operating characteristics of the multivariate $\alpha$-TOST both theoretically and via an extensive simulation study considering cases relevant for real-world analyses -- i.e.,~relatively small sample sizes, unknown and heterogeneous variances, and different correlation structures -- and show the superior finite-sample properties of the multivariate $\alpha$-TOST compared to its conventional counterpart. We finally re-visit a case study on ticlopidine hydrochloride and compare both methods when simultaneously assessing bioequivalence for multiple pharmacokinetic parameters., Comment: 34 pages, 13 figures. To be published in: Statistics in Medicine

The sparse-group lasso performs both variable and group selection, making simultaneous use of the strengths of the lasso and group lasso. It has found widespread use in genetics, a field that regularly involves the analysis of high-dimensional data, due to its sparse-group penalty, which allows it to utilize grouping information. However, the sparse-group lasso can be computationally more expensive than both the lasso and group lasso, due to the added shrinkage complexity, and its additional hyper-parameter that needs tuning. In this paper a novel dual feature reduction method, Dual Feature Reduction (DFR), is presented that uses strong screening rules for the sparse-group lasso and the adaptive sparse-group lasso to reduce their input space before optimization. DFR applies two layers of screening and is based on the dual norms of the sparse-group lasso and adaptive sparse-group lasso. Through synthetic and real numerical studies, it is shown that the proposed feature reduction approach is able to drastically reduce the computational cost in many different scenarios., Comment: 46 pages, 18 figures, 41 tables

Tuning the regularization parameter in penalized regression models is an expensive task, requiring multiple models to be fit along a path of parameters. Strong screening rules drastically reduce computational costs by lowering the dimensionality of the input prior to fitting. We develop strong screening rules for group-based Sorted L-One Penalized Estimation (SLOPE) models: Group SLOPE and Sparse-group SLOPE. The developed rules are applicable for the wider family of group-based OWL models, including OSCAR. Our experiments on both synthetic and real data show that the screening rules significantly accelerate the fitting process. The screening rules make it accessible for group SLOPE and sparse-group SLOPE to be applied to high-dimensional datasets, particularly those encountered in genetics., Comment: 31 pages, 18 figures, 10 tables

Inference methods for computing confidence intervals in parametric settings usually rely on consistent estimators of the parameter of interest. However, it may be computationally and/or analytically burdensome to obtain such estimators in various parametric settings, for example when the data exhibit certain features such as censoring, misclassification errors or outliers. To address these challenges, we propose a simulation-based inferential method, called the implicit bootstrap, that remains valid regardless of the potential asymptotic bias of the estimator on which the method is based. We demonstrate that this method allows for the construction of asymptotically valid percentile confidence intervals of the parameter of interest. Additionally, we show that these confidence intervals can also achieve second-order accuracy. We also show that the method is exact in three instances where the standard bootstrap fails. Using simulation studies, we illustrate the coverage accuracy of the method in three examples where standard parametric bootstrap procedures are computationally intensive and less accurate in finite samples.

Whether diffuse mismatch model for electrons (DMMe) hold true in more general cases remains largely unexplored, especially in cases where at least one material does not behave like a free-electron metal and/or the interface is smooth enough to allow non-diffuse transmission of electrons. In this study, DMMe was proposed to predict the thermal conductance of metal-metal interfaces. A set of aluminum-X samples (X = Cu, Ag, Fe, Ni) were grown and the time domain thermoreflectance (TDTR) technique was used to measure the metal-metal interface conductance. It was then compared to the two variants of the DMMe-using both a crude theory based on free-electron metals and accurate band structures provided by density functional theory., Comment: 34 Pages, 15 Figures

In the previous studies, the ultraviolet light thermoreflectance (UV-TDTR) signal from bulk semiconducting samples cannot be well explained by a thermal models based on the assumption that the heat is both absorbed and probed near the surface. A thermoreflectance (TDTR) technique was developed to directly excite semiconductors using UV-TDTR. At {\lambda} = 400nm, the photon energy is much greater than most semiconducting bandgaps, potentially allowing semiconducting transducers to absorb light within about 10 nm of the surface, potentially enabling direct measurements of semiconductor-semiconductor interfaces. The thermoreflectance coefficient for some direct-bandgap semiconducting materials was measured. Thermal transport models were fitted to thermorefectance data collected from the system. The differences in the signal for semiconductors with different doping and nanostructure features were also studied, which should change the recombination rate. No significant changes to the signal were observed., Comment: 20 Pages, 5 Figures

The viscous behavior of fluids can be observed in numerous everyday situations. Therefore, it is reasonable to assume that people, since they are usually not specialists in fluids' flow behavior, possess na\"ive conceptions about the viscous behavior of fluids. These conceptions more or less deviate from corresponding scientific explanations. Qualitative studies with preschool children and secondary school students from Germany have already identified various na\"ive conceptions about the viscous behavior of fluids (e.g., that the density or stickiness of a substance explains its viscous behavior). Within the present study, we explore the question of whether similar na\"ive conceptions can also be found among adults around the globe. To this end, based on previous research, an online questionnaire was developed to survey adults worldwide regarding their na\"ive conceptions about the viscous behavior of fluids. The survey was conducted anonymously, online, and voluntarily in spring 2023; participants were recruited via SurveySwap. A total of 406 adults from all regions of the world (primarily Europe and North America) participated in the survey. In this paper, we report and discuss the main findings of this online survey.

In this manuscript, a new high-dimensional approach for simultaneous variable and group selection is proposed, called sparse-group SLOPE (SGS). SGS achieves false discovery rate control at both variable and group levels by incorporating the SLOPE model into a sparse-group framework and exploiting grouping information. A proximal algorithm is implemented for fitting SGS that works for both Gaussian and Binomial distributed responses. Through the analysis of both synthetic and real datasets, the proposed SGS approach is found to outperform other existing lasso- and SLOPE-based models for bi-level selection and prediction accuracy. Further, model selection and noise estimation approaches for selecting the tuning parameter of the regularisation model are proposed and explored., Comment: 45 pages, 25 figures, 9 tables

This short contribution reports the development of a test for assessing middle school students' physics proficiency via multiple-choice single-select items in German language. The test assesses students' content and procedural knowledge across various content areas that are typical of physics education at the middle-school level and is based on adapted items developed within the Third International Mathematics and Science Study (TIMSS). We report the study design we used to develop this test, as well as the results and selected parameters regarding the test's psychometric quality.

To date, there is a lack of research on learning environments for pre-service physics teachers that allow them to learn and practise diagnosing students' conceptions that are (currently) not covered in physics education textbooks (e.g. students' conceptions about viscosity). In this study, we developed and piloted such a learning environment, which was implemented and piloted twice in a seminar for pre-service physics teachers. As coping with a diagnostic process is particularly demanding for pre-service physics teachers, our accompanying research aims to identify learning barriers within our developed learning environment. The results indicate that the participants experience the learning environment with varying degrees of difficulty. One main difficulty for pre-service physics teachers seems to be in interconnecting their content knowledge with their pedagogical content knowledge in the diagnostic process., Comment: Preprint of an article published in The Physics Educator

We present a new domain-agnostic synthesis technique for generating programs from input-output examples. Our method, called metric program synthesis, relaxes the well-known observational equivalence idea (used widely in bottom-up enumerative synthesis) into a weaker notion of observational similarity, with the goal of reducing the search space that the synthesizer needs to explore. Our method clusters programs into equivalence classes based on a distance metric and constructs a version space that compactly represents "approximately correct" programs. Then, given a "close enough" program sampled from this version space, our approach uses a distance-guided repair algorithm to find a program that exactly matches the given input-output examples. We have implemented our proposed metric program synthesis technique in a tool called SyMetric and evaluate it in three different domains considered in prior work. Our evaluation shows that SyMetric outperforms other domain-agnostic synthesizers that use observational equivalence and that it achieves results competitive with domain-specific synthesizers that are either designed for or trained on those domains.

An important challenge in statistical analysis lies in controlling the estimation bias when handling the ever-increasing data size and model complexity of modern data settings. In this paper, we propose a reliable estimation and inference approach for parametric models based on the Just Identified iNdirect Inference estimator (JINI). The key advantage of our approach is that it allows to construct a consistent estimator in a simple manner, while providing strong bias correction guarantees that lead to accurate inference. Our approach is particularly useful for complex parametric models, as it allows to bypass the analytical and computational difficulties (e.g., due to intractable estimating equation) typically encountered in standard procedures. The properties of JINI (including consistency, asymptotic normality, and its bias correction property) are also studied when the parameter dimension is allowed to diverge, which provide the theoretical foundation to explain the advantageous performance of JINI in increasing dimensional covariates settings. Our simulations and an alcohol consumption data analysis highlight the practical usefulness and excellent performance of JINI when data present features (e.g., misclassification, rounding) as well as in robust estimation.

The primary difficulty in understanding the sources and processes that powered cosmic reionization is that it is not possible to directly probe the ionizing Lyman Continuum (LyC) radiation at that epoch as those photons have been absorbed by the intervening neutral hydrogen in the IGM on their way to us. It is therefore imperative to build a model to accurately predict LyC emission using other properties of galaxies in the reionization era. In recent years, studies have shown that the LyC emission from galaxies may be correlated to their Lya emission. Here, we study this correlation by analyzing thousands of galaxies at high-z in the SPHINX cosmological simulation. We post-process these galaxies with the Lya radiative transfer code RASCAS and analyze the Lya - LyC connection. We find that the Lya and LyC luminosities are strongly correlated with each other, although with dispersion. There is a positive correlation between Lya and LyC escape fractions in the brightest Lya emitters (>$10^{41}$ erg/s), similar to the recent observational studies. However, when we also include fainter Lya emitters (LAEs), the correlation disappears, which suggests that the observed relationship may be driven by selection effects. We also find that bright LAEs are dominant contributors to reionization ($> 10^{40}$ erg/s galaxies contribute $> 90\%$ of LyC emission). Finally, we build predictive models using multivariate linear regression where we use the physical and the Lya properties of simulated galaxies to predict their intrinsic and escaping LyC luminosities with a high degree of accuracy. We find that the most important galaxy properties to predict the escaping LyC luminosity of a galaxy are its escaping Lya luminosity, gas mass, gas metallicity, and SFR. These models can be very useful to predict LyC emissions from galaxies and can help us identify the sources of reionization., Comment: Accepted to Astronomy and Astrophysics (A&A) Journal. 27 pages, 21 Figures

In this work, a Landauer approach enabled by the Frequency Domain Perfectly Matched Layer Method (FDPML) is used to study phonon transport in a series of large 2D domains with randomly embedded nanoparticles over a wide range of nanoparticle loadings and wavelengths. The effect of nanoparticle packing density on the mean free path and localization length is characterized. We observe that in the Mie scattering regime, the independent scattering approximation is valid up to volume fractions exceeding 10% and often higher depending on scattering parameter, indicating the mean free path can usually be calculated much less expensively using the number density and the scattering cross-section of a single scatterer. We also study localization lengths and their dependence on particle loading. In the case of heavy particles in a lighter matrix, we have been able to observe localization only at volume fractions >30% using the Landauer approach and only for high frequency modes, exceeding the vibration frequencies of the embedded nanoparticles. Using modal analysis we show that localization in nanoparticle laden materials is primarily due to energetic confinement rather than Anderson localization. Subsequently, we show that by using light particles in a heavy matrix the fraction of confined modes can be substantially increased.

Countries officially record the number of COVID-19 cases based on medical tests of a subset of the population with unknown participation bias. For prevalence estimation, the official information is typically discarded and, instead, small random survey samples are taken. We derive (maximum likelihood and method of moment) prevalence estimators, based on a survey sample, that additionally utilize the official information, and that are substantially more accurate than the simple sample proportion of positive cases. Put differently, using our estimators, the same level of precision can be obtained with substantially smaller survey samples. We take into account the possibility of measurement errors due to the sensitivity and specificity of the medical testing procedure. The proposed estimators and associated confidence intervals are implemented in the companion open source R package cape.

An important challenge in statistical analysis lies in controlling the bias of estimators due to the ever-increasing data size and model complexity. Approximate numerical methods and data features like censoring and misclassification often result in analytical and/or computational challenges when implementing standard estimators. As a consequence, consistent estimators may be difficult to obtain, especially in complex and/or high dimensional settings. In this paper, we study the properties of a general simulation-based estimation framework that allows to construct bias corrected consistent estimators. We show that the considered approach leads, under more general conditions, to stronger bias correction properties compared to alternative methods. Besides its bias correction advantages, the considered method can be used as a simple strategy to construct consistent estimators in settings where alternative methods may be challenging to apply. Moreover, the considered framework can be easily implemented and is computationally efficient. These theoretical results are highlighted with simulation studies of various commonly used models, including the negative binomial regression (with and without censoring) and the logistic regression (with and without misclassification errors). Additional numerical illustrations are provided in the supplementary materials.

An important challenge in statistical analysis concerns the control of the finite sample bias of estimators. This problem is magnified in high-dimensional settings where the number of variables $p$ diverges with the sample size $n$, as well as for nonlinear models and/or models with discrete data. For these complex settings, we propose to use a general simulation-based approach and show that the resulting estimator has a bias of order $\mathcal{O}(0)$, hence providing an asymptotically optimal bias reduction. It is based on an initial estimator that can be slightly asymptotically biased, making the approach very generally applicable. This is particularly relevant when classical estimators, such as the maximum likelihood estimator, can only be (numerically) approximated. We show that the iterative bootstrap of Kuk (1995) provides a computationally efficient approach to compute this bias reduced estimator. We illustrate our theoretical results in simulation studies for which we develop new bias reduced estimators for the logistic regression, with and without random effects. These estimators enjoy additional properties such as robustness to data contamination and to the problem of separability., Comment: arXiv admin note: substantial text overlap with arXiv:1907.11541

Complex time series models such as (the sum of) ARMA$(p,q)$ models with additional noise, random walks, rounding errors and/or drifts are increasingly used for data analysis in fields such as biology, ecology, engineering and economics where the length of the observed signals can be extremely large. Performing inference on and/or prediction from these models can be highly challenging for several reasons: (i) the data may contain outliers that can adversely affect the estimation procedure; (ii) the computational complexity can become prohibitive when models include more than just a few parameters and/or the time series are large; (iii) model building and/or selection adds another layer of (computational) complexity to the previous task; and (iv) solutions that address (i), (ii) and (iii) simultaneously do not exist in practice. For this reason, this paper aims at jointly addressing these challenges by proposing a general framework for robust two-step estimation based on a bounded influence M-estimator of the wavelet variance. In this perspective, we first develop the conditions for the joint asymptotic normality of the latter estimator thereby providing the necessary tools to perform (direct) inference for scale-based analysis of signals. Taking advantage of the model-independent weights of this first-step estimator that are computed only once, we then develop the asymptotic properties of two-step robust estimators using the framework of the Generalized Method of Wavelet Moments (GMWM), hence defining the Robust GMWM (RGMWM) that we then use for robust model estimation and inference in a computationally efficient manner even for large time series. Simulation studies illustrate the good finite sample performance of the RGMWM estimator and applied examples highlight the practical relevance of the proposed approach.