Miracle-dz/UniFAR-FTUs huggingface dataset:
Average Power and $\lambda$-power in Multiple Testing Scenarios when the Benjamini-Hochberg False Discovery Rate Procedure is Used

We discuss several approaches to defining power in studies designed around the Benjamini-Hochberg (BH) false discovery rate (FDR) procedure. We focus primarily on the \textit{average power} and the $\lambda$-\textit{power}, which are the expected true positive fraction and the probability that the true positive fraction exceeds $\lambda$, respectively. We show that the average power converges as the number of simultaneous tests tends to infinity, to a limit that is nearly equivalent to the power introduced independently by \cite{JungSH:2005} and by \cite{LiuP:2007}. Furthermore, we prove a CLT which allows asymptotic approximation to the $\lambda$-power. Moreover, we prove SLLNs and CLTs for all quantities connected to the BH-FDR procedure: the positive call fraction, true positive fraction, and false discovery fraction, with full characterization of almost sure limits and limits in distribution. We discuss ramifications of the CLT for the false discovery fraction, introducing a procedure which allows tighter control of the false discovery fraction than the BH-FDR that can be used at the design and analysis steps. We conduct a large simulation study covering a fairly substantial portion of the space of possible inputs. We show its application in design of a biomarker study, a micro-array experiment and a GWAS study.
Detecting Faltering Growth in Children via Minimum Random Slopes

A child is considered to have faltered growth when increases in their height or weight starts to decline relative to a suitable comparison population. However, there is currently a lack of consensus on both the choice of anthropometric indexes for characterizing growth over time and the operational definition of faltering. Cole's classic conditional standard deviation scores is a popular metric but can be problematic, since it only utilizes two data points and relies on having complete data. In the existing literature, arbitrary thresholds are often used to define faltering, which may not be appropriate for all populations. In this article, we propose to assess faltering via minimum random slopes (MRS) derived from a piecewise linear mixed model. When used in conjunction with mixture model-based classification, MRS provides a viable method for identifying children that have faltered, without being dependent upon arbitrary standards. We illustrate our work via a simulation study and apply it to a case study based on a birth cohort within the Healthy Birth, Growth and Development knowledge integration (HBGDki) project funded by the Bill and Melinda Gates Foundation.
An approximate Bayesian inference on propensity score estimation under unit nonresponse

Nonresponse weighting adjustment using the response propensity score is a popular tool for handling unit nonresponse. Statistical inference after the nonresponse weighting adjustment is an important problem and Taylor linearization method is often used to reflect the effect of estimating the propensity score weights. In this article, we propose an approximate Bayesian approach to handle unit nonresponse with parametric model assumptions on the response probability, but without model assumptions for the outcome variable. The proposed Bayesian method is calibrated to the frequentist inference in that the credible region obtained from the posterior distribution asymptotically matches to the frequentist confidence interval obtained from the Taylor linearization method. The proposed Bayesian method is also extended to incorporate the auxiliary information from full sample. Results from limited simulation studies confirm the validity of the proposed methods. The proposed method is applied to data from a Korean longitudinal survey.
L\'evy area of fractional Ornstein-Uhlenbeck process and parameter estimation

In this paper, we study the estimation problem of an unknown drift parameter matrix for fractional Ornstein-Uhlenbeck process in multi-dimensional setting. By using rough path theory, we propose pathwise rough path estimators based on both continuous and discrete observations of a single path. The approach is applicable to the high-frequency data. To formulate the parameter estimators, we define a theory of pathwise Itô integrals with respect to fractional Brownian motion. By showing the regularity of fractional Ornstein-Uhlenbeck processes and the long time asymptotic behaviour of the associated Lévy area processes, we prove that the estimators are strong consistent and pathwise stable. Numerical studies and simulations are also given in this paper.
β
 decay and 
β
-delayed neutron decay of the 
N=82
 nucleus 
In8249131

The half-lives of three β-decaying states of 13149In82 have been measured with the GRIFFIN γ-ray spectrometer at the TRIUMF-ISAC facility to be T1/2(1/2−)=328(15)ms, T1/2(9/2+)=265(8)ms, and T1/2(21/2+)=323(55)ms, respectively. The first observation of γ rays following the βn decay of 131In into 130Sn is reported. The β-delayed neutron emission probability is determined to be P1n=12(7)% for the 21/2+ state and 2.3(3)% from the combined 1/2− and 9/2+ states of 13149In82 observed in this experiment. A significant expansion of the decay scheme of 131In, including 17 new excited states and 35 new γ-ray transitions in 13150Sn81 is also reported. This leads to large changes in the deduced β-branching ratios to some of the low-lying states of 131Sn compared to previous work with implications for the strength of the first-forbidden β transitions in the vicinity of doubly magic 13250Sn82. (Less)
A Nonparametric Bayesian Methodology for Synthesizing Residential Solar Generation and Demand Data

The uptake of behind-the-meter distributed energy resources in low-voltage distribution networks has progressed to a point where phenomena including overvoltage and reverse power flow are emerging. The limited nature of supervision and control on these networks require novel tools for operation and planning in this environment. In this paper, we propose a methodology for synthesizing stochastic demand and generation profiles for unobserved customers with rooftop PV, called prosumers. The proposed model bridges the gap between the limited available empirical data, and the large amount of high-quality, stochastic demand and generation data required for probabilistic analysis. The approach employs clustering analysis and a Dirichlet-categorical hierarchical model of the features of unobserved prosumers. Based on the data of clusters of prosumers, Markov chain models of demand and generation profiles are constructed from empirical data, and synthetic demand profiles are subsequently sampled from these. The sampled traces are cross-validated and show a good statistical fit to the observed data. Two case studies are considered to confirm the validity of the proposed methodology. The first studies the impact of behavioral differences on the synthetic demand profiles, while the second looks at the impact of varying solar generation penetration on demand profiles.
Structural Equation Modeling and simultaneous clustering through the Partial Least Squares algorithm

The identification of different homogeneous groups of observations and their appropriate analysis in PLS-SEM has become a critical issue in many appli- cation fields. Usually, both SEM and PLS-SEM assume the homogeneity of all units on which the model is estimated, and approaches of segmentation present in literature, consist in estimating separate models for each segments of statistical units, which have been obtained either by assigning the units to segments a priori defined. However, these approaches are not fully accept- able because no causal structure among the variables is postulated. In other words, a modeling approach should be used, where the obtained clusters are homogeneous with respect to the structural causal relationships. In this paper, a new methodology for simultaneous non-hierarchical clus- tering and PLS-SEM is proposed. This methodology is motivated by the fact that the sequential approach of applying first SEM or PLS-SEM and second the clustering algorithm such as K-means on the latent scores of the SEM/PLS-SEM may fail to find the correct clustering structure existing in the data. A simulation study and an application on real data are included to evaluate the performance of the proposed methodology.
Conditionally Conjugate Mean-Field Variational Bayes for Logistic Models

Variational Bayes (VB) is a common strategy for approximate Bayesian inference, but simple methods are only available for specific classes of models including, in particular, representations having conditionally conjugate constructions within an exponential family. Models with logit components are an apparently notable exception to this class, due to the absence of conjugacy between the logistic likelihood and the Gaussian priors for the coefficients in the linear predictor. To facilitate approximate inference within this widely used class of models, Jaakkola and Jordan (2000) proposed a simple variational approach which relies on a family of tangent quadratic lower bounds of logistic log-likelihoods, thus restoring conjugacy between these approximate bounds and the Gaussian priors. This strategy is still implemented successfully, but less attempts have been made to formally understand the reasons underlying its excellent performance. To cover this key gap, we provide a formal connection between the above bound and a recent Polya-gamma data augmentation for logistic regression. Such a result places the computational methods associated with the aforementioned bounds within the framework of variational inference for conditionally conjugate exponential family models, thereby allowing recent advances for this class to be inherited also by the methods relying on Jaakkola and Jordan (2000).
Bayesian Model Selection for a Class of Spatially-Explicit Capture Recapture Models

A vast amount of ecological knowledge generated recently has hinged upon the ability of model selection methods to discriminate among various ecological hypotheses. The last decade has seen the rise of Bayesian hierarchical models in ecology. Consequently, popular tools, such as the AIC, become largely inapplicable and other tools are not universally applicable. We focus on a class of competing Bayesian spatially explicit capture recapture (SECR) models and first apply some of the recommended Bayesian model selection tools: (1) Bayes Factor - using (a) Gelfand-Dey (b) harmonic mean methods, (2) DIC, (3) WAIC and (4) the posterior predictive loss function. In all, we evaluate 25 variants of model selection tools in our study. We evaluate these model selection tools from the standpoint of model selection and parameter estimation by contrasting the choice recommended by a tool with a `true' model. In all, we generate 120 simulated data sets using the true model and assess the frequency with which the true model is selected and how well the tool estimates N (population size). We find that when information content is low, no particular tool can be recommended to help realise, simultaneously, both the goals of model selection and parameter estimation. In such scenarios, we recommend that practitioners utilise our application of Bayes Factor for parameter estimation and recommend the posterior predictive loss approach for model selection when information content is low. When both the objectives are taken together, we recommend the use of our applications of Bayes Factor for Bayesian SECR models. Our study reveals that although new model selection tools are emerging (eg: WAIC) in the applied statistics literature, an uncritical absorption of these new tools (i.e. without assessing their efficacies for the problem at hand) into ecological practice may mislead inferences.
Constructing Stabilized Dynamic Treatment Regimes for Censored Data

Stabilized dynamic treatment regimes are sequential decision rules for individual patients that not only adaptive throughout the disease progression but also remain consistent over time in format. The estimation of stabilized dynamic treatment regimes becomes more complicated when the clinical outcome of interest is a survival time subject to censoring. To address this challenge, we propose two novel methods, censored shared-Q-learning and censored shared-O-learning. Both methods incorporate clinical preferences into a qualitative rule, where the parameters indexing the decision rules are shared across different stages and estimated simultaneously. We use extensive simulation studies to demonstrate the superior performance of the proposed methods. The methods are further applied to the Framingham Study to derive treatment rules for cardiovascular disease.
Multilevel Models Allow Modular Specification of What and Where to Regularize, Especially in Small Area Estimation

Through the lense of multilevel model (MLM) specification and regularization, this is a connect-the-dots introductory summary of Small Area Estimation, e.g. small group prediction informed by a complex sampling design. While a comprehensive book is (Rao and Molina 2015), the goal of this paper is to get interested researchers up to speed with some current developments. We first provide historical context of two kinds of regularization: 1) the regularization 'within' the components of a predictor and 2) the regularization 'between' outcome and predictor. We focus on the MLM framework as it allows the analyst to flexibly control the targets of the regularization. The flexible control is useful when analysts want to overcome shortcomings in design-based estimates. We'll describe the precision deficiencies (high variance) typical of design-based estimates of small groups. We then highlight an interesting MLM example from (Chaudhuri and Ghosh 2011) that integrates both kinds of regularization (between and within). The key idea is to use the design-based variance to control the amount of 'between' regularization and prior information to regularize the components 'within' a predictor. The goal is to let the design-based estimate have authority (when precise) but defer to a model-based prediction when imprecise. We conclude by discussing optional criteria to incorporate into a MLM prediction and possible entrypoints for extensions.
Markov-Modulated Linear Regression

Classical linear regression is considered for a case when regression parameters depend on the external random environment. The last is described as a continuous time Markov chain with finite state space. Here the expected sojourn times in various states are additional regressors. Necessary formulas for an estimation of regression parameters have been derived. The numerical example illustrates the results obtained.
Generalized simultaneous component analysis of binary and quantitative data

In the current era of systems biology research, there is a need for the integrative analysis of binary and quantitative genomics data sets measured on the same objects. One standard tool of exploring the underlying dependence structure present in multiple quantitative data sets is the simultaneous component analysis (SCA) model. However, it does not have any provisions when a part of the data are binary. To this end, we propose the generalized SCA (GSCA) model, which takes into account the distinct mathematical properties of binary and quantitative measurements in the maximum likelihood framework. Like in the SCA model, a common low‐dimensional subspace is assumed to represent the shared information between these two distinct types of measurements. To achieve a low rank solution, we propose to use a concave variant of the nuclear norm penalty. An efficient majorization algorithm is developed to fit this model with different concave penalties. Realistic simulations (low signal‐to‐noise ratio and highly imbalanced binary data) are used to evaluate the performance of the proposed model in recovering the underlying structure. Also, a missing value based cross‐validation procedure is implemented for model selection. We illustrate the usefulness of the GSCA model for exploratory data analysis of quantitative gene expression and binary copy number aberration measurements obtained from the GDSC1000 data sets.
An outlier-resistant indicator of anomalies among inter-laboratory comparison data with associated uncertainty

A new robust pairwise statistic, the pairwise median scaled difference (MSD), is proposed for the detection of anomalous location/uncertainty pairs in heteroscedastic interlaboratory study data with associated uncertainties. The distribution for the IID case is presented and approximate critical values for routine use are provided. The determination of observationspecific quantiles and p-values for heteroscedastic data, using parametric bootstrapping, is demonstrated by example. It is shown that the statistic has good power for detecting anomalies compared to a previous pairwise statistic, and offers much greater resistance to multiple
On Curvature-aided Incremental Aggregated Gradient Methods

This paper studies an acceleration technique for incremental aggregated gradient methods which exploits curvature information for solving strongly convex finite sum optimization problems. These optimization problems of interest arise in large-scale learning applications relevant to machine learning systems. The proposed methods utilizes a novel curvature-aided gradient tracking technique to produce gradient estimates using the aids of Hessian information during computation. We propose and analyze two curvature-aided methods --- the first method, called curvature-aided incremental aggregated gradient (CIAG) method, can be developed from the standard gradient method and it computes an $\epsilon$-optimal solution using ${\cal O}( \kappa \log ( 1 / \epsilon ) )$ iterations for a small $\epsilon$; the second method, called accelerated CIAG (A-CIAG) method, incorporates Nesterov's acceleration into CIAG and requires ${\cal O}( \sqrt{\kappa} \log ( 1 / \epsilon ) )$ iterations for a small $\epsilon$, where $\kappa$ is the problem's condition number. Importantly, the asymptotic convergence rates above are the same as those of the full gradient and accelerated full gradient methods, respectively, and they are independent of the number of component functions involved. The proposed methods are significantly faster than the state-of-the-art methods, especially for large-scale problems with a massive amount of data. The source codes are available at this https URL
Multi-Resolution Filters for Massive Spatio-Temporal Data

Abstract Spatio-temporal datasets are rapidly growing in size. For example, environmental variables are measured with increasing resolution by increasing numbers of automated sensors mounted on satellites and aircraft. Using such data, which are typically noisy and incomplete, the goal is to obtain complete maps of the spatio-temporal process, together with uncertainty quantification. We focus here on real-time filtering inference in linear Gaussian state-space models. At each time point, the state is a spatial field evaluated on a very large spatial grid, making exact inference using the Kalman filter computationally infeasible. Instead, we propose a multi-resolution filter (MRF), a highly scalable and fully probabilistic filtering method that resolves spatial features at all scales. We prove that the MRF matrices exhibit a particular block-sparse multi-resolution structure that is preserved under filtering operations through time. We describe connections to existing methods, including hierarchical matrices from numerical mathematics. We also discuss inference on time-varying parameters using an approximate Rao-Blackwellized particle filter, in which the integrated likelihood is computed using the MRF. Using a simulation study and a real satellite-data application, we show that the MRF strongly outperforms competing approaches. Supplementary materials include Python code for reproducing the simulations, some detailed properties of the MRF and auxiliary theoretical results.
A Shiny Update to an Old Experiment Game

ABSTRACT Games can be a powerful tool for learning about statistical methodology. Effective game design involves a fine balance between caricature and realism, to simultaneously illustrate salient concepts in a controlled setting and serve as a testament to real-world applicability. Striking that balance is particularly challenging in response surface and design domains, where real-world scenarios often play out over long time scales, during which theories are revised, model and inferential techniques are improved, and knowledge is updated. Here, I present a game, borrowing liberally from one first played over 40 years ago, which attempts to achieve that balance while reinforcing a cascade of topics in modern nonparametric response surfaces, sequential design, and optimization. The game embeds a blackbox simulation within a shiny app whose interface is designed to simulate a realistic information–availability setting, while offering a stimulating, competitive environment wherein students can try out new methodology, and ultimately appreciate its power and limitations. Interface, rules, timing with course material, and evaluation are described, along with a “case study” involving a cohort of students at Virginia Tech. Supplementary materials for this article are available online.
Neighborhood selection with application to social networks

The topic of this paper is modeling and analyzing dependence in stochastic social networks. Using a latent variable block model allows the analysis of dependence between blocks via the analysis of a latent graphical model. Our approach to the analysis of the graphical model then is based on the idea underlying the neighborhood selection scheme put forward by Meinshausen and B\"{u}hlmann (2006). However, because of the latent nature of our model, estimates have to be used in lieu of the unobserved variables. This leads to a novel analysis of graphical models under uncertainty, in the spirit of Rosenbaum et al. (2010), or Belloni et al. (2017). Lasso-based selectors, and a class of Dantzig-type selectors are studied.
Thermoelectric properties of gapped bilayer graphene

Unlike in conventional semiconductors, both the chemical potential and the band gap in bilayer graphene (BLG) can be tuned via application of external electric field. Among numerous device implications, this property also designates BLG as a candidate for high-performance thermoelectric material. In this theoretical study we have calculated the Seebeck coefficients for abrupt interface separating weakly- and heavily-doped areas in BLG, and for a more realistic rectangular sample of mesoscopic size, contacted by two electrodes. For a given band gap () and temperature (T) the maximal Seebeck coefficient is close to the Goldsmid–Sharp value , the deviations can be approximated by the asymptotic expression , with the electron charge −e, the Boltzmann constant , and . Surprisingly, the effects of trigonal warping term in the BLG low-energy Hamiltonian are clearly visible at few-Kelvin temperatures, for all accessible values of meV. We also show that thermoelectric figure of merit is noticeably enhanced (ZT > 3) when a rigid substrate suppresses out-of-plane vibrations, reducing the contribution from ZA phonons to the thermal conductivity.