cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.2082718.v1
title: Robust Maximum Association Estimators

description: The maximum association between two multivariate variables X and Y is defined as the maximal value that a bivariate association measure between one-dimensional projections αtX and βtY can attain. Taking the Pearson correlation as projection index results in the first canonical correlation coefficient. We propose to use more robust association measures, such as Spearman’s or Kendall’s rank correlation, or association measures derived from bivariate scatter matrices. We study the robustness of the proposed maximum association measures and the corresponding estimators of the coefficients yielding the maximum association. In the important special case of Y being univariate, maximum rank correlation estimators yield regression estimators that are invariant against monotonic transformations of the response. We obtain asymptotic variances for this special case. It turns out that maximum rank correlation estimators combine good efficiency and robustness properties. Simulations and a real data example illustrate the robustness and the power for handling nonlinear relationships of these estimators. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.2069721.v3
title: Covariance Regression Analysis

description: This article introduces covariance regression analysis for a <i>p</i>-dimensional response vector. The proposed method explores the regression relationship between the <i>p</i>-dimensional covariance matrix and auxiliary information. We study three types of estimators: maximum likelihood, ordinary least squares, and feasible generalized least squares estimators. Then, we demonstrate that these regression estimators are consistent and asymptotically normal. Furthermore, we obtain the high dimensional and large sample properties of the corresponding covariance matrix estimators. Simulation experiments are presented to demonstrate the performance of both regression and covariance matrix estimates. An example is analyzed from the Chinese stock market to illustrate the usefulness of the proposed covariance regression model. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.5536992
title: Raw data for integrated, ultra-compact high-Q silicon nitride microresonators for low-repetition-rate soliton microcombs

description: This file includes the raw data for integrated, ultra-compact high-Q silicon nitride microresonators for low-repetition-rate soliton microcombs
DOI: 10.5281/zenodo.4605595
title: Raw data for integrated, ultra-compact high-Q silicon nitride microresonators for low-repetition-rate soliton microcombs

description: This file includes the raw data for integrated, ultra-compact high-Q silicon nitride microresonators for low-repetition-rate soliton microcombs
DOI: 10.5281/zenodo.5536940
title: Nedystoma dayi RB0467 NOVOGENE decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5536939
title: Nedystoma dayi RB0467 NOVOGENE decontaminated FSCR

description: .
DOI: 10.15468/2qgtev
title: Taxonomic note on Sarata tephrella Ragonot (Lepidoptera: Pyraloidea: Pyralidae: Phycitinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ferris, Clifford D., Nordin, John S. (2004): Taxonomic note on Sarata tephrella Ragonot (Lepidoptera: Pyraloidea: Pyralidae: Phycitinae). Zootaxa 569 (1): 1-7, DOI: 10.11646/zootaxa.569.1.1, URL: https://biotaxa.org/Zootaxa/article/view/zootaxa.569.1.1
DOI: 10.6084/m9.figshare.15059718
title: Data-Beibu Gulf.xls

description: Dual stable isotopes of water (i.e., δD and δ<sup>18</sup>O), temperature, salinity, and other environmental factors (including chlorophyll <i>a</i> (Chla), NO<sub>3</sub><sup>-</sup>, and NH<sub>4</sub><sup>+</sup>) were collected from 44 stations located in the Beibu Gulf (BG) during summer, fall, and winter. These data were applied to improve our understanding of water mass processes and their impact on the environment of the gulf.
DOI: 10.6084/m9.figshare.4572697
title: Conditional Spectral Analysis of Replicated Multiple Time Series With Application to Nocturnal Physiology

description: This article considers the problem of analyzing associations between power spectra of multiple time series and cross-sectional outcomes when data are observed from multiple subjects. The motivating application comes from sleep medicine, where researchers are able to noninvasively record physiological time series signals during sleep. The frequency patterns of these signals, which can be quantified through the power spectrum, contain interpretable information about biological processes. An important problem in sleep research is drawing connections between power spectra of time series signals and clinical characteristics; these connections are key to understanding biological pathways through which sleep affects, and can be treated to improve, health. Such analyses are challenging as they must overcome the complicated structure of a power spectrum from multiple time series as a complex positive-definite matrix-valued function. This article proposes a new approach to such analyses based on a tensor-product spline model of Cholesky components of outcome-dependent power spectra. The approach flexibly models power spectra as nonparametric functions of frequency and outcome while preserving geometric constraints. Formulated in a fully Bayesian framework, a Whittle likelihood-based Markov chain Monte Carlo (MCMC) algorithm is developed for automated model fitting and for conducting inference on associations between outcomes and spectral measures. The method is used to analyze data from a study of sleep in older adults and uncovers new insights into how stress and arousal are connected to the amount of time one spends in bed. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3826731
title: Density Level Sets: Asymptotics, Inference, and Visualization

description: We study the plug-in estimator for density level sets under Hausdorff loss. We derive asymptotic theory for this estimator, and based on this theory, we develop two bootstrap confidence regions for level sets. We introduce a new technique for visualizing density level sets, even in multidimensions, which is easy to interpret and efficient to compute. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3826731.v2
title: Density Level Sets: Asymptotics, Inference, and Visualization

description: We study the plug-in estimator for density level sets under Hausdorff loss. We derive asymptotic theory for this estimator, and based on this theory, we develop two bootstrap confidence regions for level sets. We introduce a new technique for visualizing density level sets, even in multidimensions, which is easy to interpret and efficient to compute. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4876799.v4
title: A Probabilistic Record Linkage Model for Survival Data

description: In the absence of a unique identifier, combining information from multiple files relies on partially identifying variables (e.g., gender, initials). With a record linkage procedure, these variables are used to distinguish record pairs that belong together (matches) from record pairs that do not belong together (nonmatches). Generally, the combined strength of the partially identifying variables is too low causing imperfect linkage; some true nonmatches are identified as match and, on the other hand, some true matches as nonmatch. To avoid bias in further analyses, it is necessary to correct for imperfect linkage. In this article, pregnancy data from the Perinatal Registry of the Netherlands were used to estimate the associations between the (baseline) characteristics from the first delivery and the time to a second delivery. Because of privacy regulations, no unique identifier was available to determine which pregnancies belonged to the same woman. To deal with imperfect linkage in a time-to-event setting, where we have a file with baseline characteristics and a file with event times, we developed a joint model in which the record linkage procedure and the time-to-event analysis are performed simultaneously. R code and example data are available as online supplemental material.
DOI: 10.6084/m9.figshare.4572697.v3
title: Conditional Spectral Analysis of Replicated Multiple Time Series With Application to Nocturnal Physiology

description: This article considers the problem of analyzing associations between power spectra of multiple time series and cross-sectional outcomes when data are observed from multiple subjects. The motivating application comes from sleep medicine, where researchers are able to noninvasively record physiological time series signals during sleep. The frequency patterns of these signals, which can be quantified through the power spectrum, contain interpretable information about biological processes. An important problem in sleep research is drawing connections between power spectra of time series signals and clinical characteristics; these connections are key to understanding biological pathways through which sleep affects, and can be treated to improve, health. Such analyses are challenging as they must overcome the complicated structure of a power spectrum from multiple time series as a complex positive-definite matrix-valued function. This article proposes a new approach to such analyses based on a tensor-product spline model of Cholesky components of outcome-dependent power spectra. The approach flexibly models power spectra as nonparametric functions of frequency and outcome while preserving geometric constraints. Formulated in a fully Bayesian framework, a Whittle likelihood-based Markov chain Monte Carlo (MCMC) algorithm is developed for automated model fitting and for conducting inference on associations between outcomes and spectral measures. The method is used to analyze data from a study of sleep in older adults and uncovers new insights into how stress and arousal are connected to the amount of time one spends in bed. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3826731.v1
title: Density Level Sets: Asymptotics, Inference, and Visualization

description: We study the plug-in estimator for density level sets under Hausdorff loss. We derive asymptotic theory for this estimator, and based on this theory, we develop two bootstrap confidence regions for level sets. We introduce a new technique for visualizing density level sets, even in multidimensions, which is easy to interpret and efficient to compute. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4876799
title: A Probabilistic Record Linkage Model for Survival Data

description: In the absence of a unique identifier, combining information from multiple files relies on partially identifying variables (e.g., gender, initials). With a record linkage procedure, these variables are used to distinguish record pairs that belong together (matches) from record pairs that do not belong together (nonmatches). Generally, the combined strength of the partially identifying variables is too low causing imperfect linkage; some true nonmatches are identified as match and, on the other hand, some true matches as nonmatch. To avoid bias in further analyses, it is necessary to correct for imperfect linkage. In this article, pregnancy data from the Perinatal Registry of the Netherlands were used to estimate the associations between the (baseline) characteristics from the first delivery and the time to a second delivery. Because of privacy regulations, no unique identifier was available to determine which pregnancies belonged to the same woman. To deal with imperfect linkage in a time-to-event setting, where we have a file with baseline characteristics and a file with event times, we developed a joint model in which the record linkage procedure and the time-to-event analysis are performed simultaneously. R code and example data are available as online supplemental material.
DOI: 10.6084/m9.figshare.4572697.v2
title: Conditional Spectral Analysis of Replicated Multiple Time Series With Application to Nocturnal Physiology

description: This article considers the problem of analyzing associations between power spectra of multiple time series and cross-sectional outcomes when data are observed from multiple subjects. The motivating application comes from sleep medicine, where researchers are able to noninvasively record physiological time series signals during sleep. The frequency patterns of these signals, which can be quantified through the power spectrum, contain interpretable information about biological processes. An important problem in sleep research is drawing connections between power spectra of time series signals and clinical characteristics; these connections are key to understanding biological pathways through which sleep affects, and can be treated to improve, health. Such analyses are challenging as they must overcome the complicated structure of a power spectrum from multiple time series as a complex positive-definite matrix-valued function. This article proposes a new approach to such analyses based on a tensor-product spline model of Cholesky components of outcome-dependent power spectra. The approach flexibly models power spectra as nonparametric functions of frequency and outcome while preserving geometric constraints. Formulated in a fully Bayesian framework, a Whittle likelihood-based Markov chain Monte Carlo (MCMC) algorithm is developed for automated model fitting and for conducting inference on associations between outcomes and spectral measures. The method is used to analyze data from a study of sleep in older adults and uncovers new insights into how stress and arousal are connected to the amount of time one spends in bed. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4742170
title: Evaluating the Quality of Survey and Administrative Data with Generalized Multitrait-Multimethod Models

description: Administrative data are increasingly important in statistics, but, like other types of data, may contain measurement errors. To prevent such errors from invalidating analyses of scientific interest, it is therefore essential to estimate the extent of measurement errors in administrative data. Currently, however, most approaches to evaluate such errors involve either prohibitively expensive audits or comparison with a survey that is assumed perfect. We introduce the “generalized multitrait-multimethod” (GMTMM) model, which can be seen as a general framework for evaluating the quality of administrative and survey data simultaneously. This framework allows both survey and administrative data to contain random and systematic measurement errors. Moreover, it accommodates common features of administrative data such as discreteness, nonlinearity, and nonnormality, improving similar existing models. The use of the GMTMM model is demonstrated by application to linked survey-administrative data from the German Federal Employment Agency on income from of employment, and a simulation study evaluates the estimates obtained and their robustness to model misspecification. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5829639.v3
title: PLEMT: A Novel Pseudolikelihood-Based EM Test for Homogeneity in Generalized Exponential Tilt Mixture Models

description: Motivated by analyses of DNA methylation data, we propose a semiparametric mixture model, namely, the generalized exponential tilt mixture model, to account for heterogeneity between differentially methylated and nondifferentially methylated subjects in the cancer group, and capture the differences in higher order moments (e.g., mean and variance) between subjects in cancer and normal groups. A pairwise pseudolikelihood is constructed to eliminate the unknown nuisance function. To circumvent boundary and nonidentifiability problems as in parametric mixture models, we modify the pseudolikelihood by adding a penalty function. In addition, the test with simple asymptotic distribution has computational advantages compared with permutation-based test for high-dimensional genetic or epigenetic data. We propose a pseudolikelihood-based expectation–maximization test, and show the proposed test follows a simple chi-squared limiting distribution. Simulation studies show that the proposed test controls Type I errors well and has better power compared to several current tests. In particular, the proposed test outperforms the commonly used tests under all simulation settings considered, especially when there are variance differences between two groups. The proposed test is applied to a real dataset to identify differentially methylated sites between ovarian cancer subjects and normal subjects. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4742170.v3
title: Evaluating the Quality of Survey and Administrative Data with Generalized Multitrait-Multimethod Models

description: Administrative data are increasingly important in statistics, but, like other types of data, may contain measurement errors. To prevent such errors from invalidating analyses of scientific interest, it is therefore essential to estimate the extent of measurement errors in administrative data. Currently, however, most approaches to evaluate such errors involve either prohibitively expensive audits or comparison with a survey that is assumed perfect. We introduce the “generalized multitrait-multimethod” (GMTMM) model, which can be seen as a general framework for evaluating the quality of administrative and survey data simultaneously. This framework allows both survey and administrative data to contain random and systematic measurement errors. Moreover, it accommodates common features of administrative data such as discreteness, nonlinearity, and nonnormality, improving similar existing models. The use of the GMTMM model is demonstrated by application to linked survey-administrative data from the German Federal Employment Agency on income from of employment, and a simulation study evaluates the estimates obtained and their robustness to model misspecification. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.5829639.v2
title: PLEMT: A Novel Pseudolikelihood-Based EM Test for Homogeneity in Generalized Exponential Tilt Mixture Models

description: Motivated by analyses of DNA methylation data, we propose a semiparametric mixture model, namely, the generalized exponential tilt mixture model, to account for heterogeneity between differentially methylated and nondifferentially methylated subjects in the cancer group, and capture the differences in higher order moments (e.g., mean and variance) between subjects in cancer and normal groups. A pairwise pseudolikelihood is constructed to eliminate the unknown nuisance function. To circumvent boundary and nonidentifiability problems as in parametric mixture models, we modify the pseudolikelihood by adding a penalty function. In addition, the test with simple asymptotic distribution has computational advantages compared with permutation-based test for high-dimensional genetic or epigenetic data. We propose a pseudolikelihood-based expectation–maximization test, and show the proposed test follows a simple chi-squared limiting distribution. Simulation studies show that the proposed test controls Type I errors well and has better power compared to several current tests. In particular, the proposed test outperforms the commonly used tests under all simulation settings considered, especially when there are variance differences between two groups. The proposed test is applied to a real dataset to identify differentially methylated sites between ovarian cancer subjects and normal subjects. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.4742170.v2
title: Evaluating the Quality of Survey and Administrative Data with Generalized Multitrait-Multimethod Models

description: Administrative data are increasingly important in statistics, but, like other types of data, may contain measurement errors. To prevent such errors from invalidating analyses of scientific interest, it is therefore essential to estimate the extent of measurement errors in administrative data. Currently, however, most approaches to evaluate such errors involve either prohibitively expensive audits or comparison with a survey that is assumed perfect. We introduce the “generalized multitrait-multimethod” (GMTMM) model, which can be seen as a general framework for evaluating the quality of administrative and survey data simultaneously. This framework allows both survey and administrative data to contain random and systematic measurement errors. Moreover, it accommodates common features of administrative data such as discreteness, nonlinearity, and nonnormality, improving similar existing models. The use of the GMTMM model is demonstrated by application to linked survey-administrative data from the German Federal Employment Agency on income from of employment, and a simulation study evaluates the estimates obtained and their robustness to model misspecification. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3798153
title: A Semiparametric Single-Index Risk Score Across Populations

description: We consider a problem motivated by issues in nutritional epidemiology, across diseases and populations. In this area, it is becoming increasingly common for diseases to be modeled by a single diet score, such as the Healthy Eating Index, the Mediterranean Diet Score, etc. For each disease and for each population, a partially linear single-index model is fit. The partially linear aspect of the problem is allowed to differ in each population and disease. However, and crucially, the single-index itself, having to do with the diet score, is common to all diseases and populations, and the nonparametrically estimated functions of the single-index are the same up to a scale parameter. Using B-splines with an increasing number of knots, we develop a method to solve the problem, and display its asymptotic theory. An application to the NIH-AARP Study of Diet and Health is described, where we show the advantages of using multiple diseases and populations simultaneously rather than one at a time in understanding the effect of increased Milk consumption. Simulations illustrate the properties of the methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3798153.v3
title: A Semiparametric Single-Index Risk Score Across Populations

description: We consider a problem motivated by issues in nutritional epidemiology, across diseases and populations. In this area, it is becoming increasingly common for diseases to be modeled by a single diet score, such as the Healthy Eating Index, the Mediterranean Diet Score, etc. For each disease and for each population, a partially linear single-index model is fit. The partially linear aspect of the problem is allowed to differ in each population and disease. However, and crucially, the single-index itself, having to do with the diet score, is common to all diseases and populations, and the nonparametrically estimated functions of the single-index are the same up to a scale parameter. Using B-splines with an increasing number of knots, we develop a method to solve the problem, and display its asymptotic theory. An application to the NIH-AARP Study of Diet and Health is described, where we show the advantages of using multiple diseases and populations simultaneously rather than one at a time in understanding the effect of increased Milk consumption. Simulations illustrate the properties of the methods. Supplementary materials for this article are available online.