cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28510976.v1
title: Integrated exclusive hypothesis test for response missing at random

description: This study presents a novel method with its computational scheme for testing the exclusive hypothesis in regression models with missing responses under the missing at random (MAR) mechanism. The proposed integrated exclusive hypothesis test addresses the challenge of testing whether a response variable is associated with at most one or multiple factors among a set of predictors, based on the inverse probability weighted (IPW) estimation and the -2-loss-difference test statistics, which are combined to form an integrated decision criterion. The asymptotic properties are established, showing high theoretical and computational efficiency. Numerical experiments demonstrate the validation and robustness of the proposed methodology. As an application, the proposed test is used on an AIDS clinical dataset collected from AIDS Clinical Trials Group Protocol 175.
DOI: 10.6084/m9.figshare.28938901
title: Fractional Dickey-Fuller test with or without prehistorical influence

description: Recently, the generalization of the standard Dickey-Fuller test to the fractional case has been proposed. This test, called the fractional Dickey-Fuller (<i>F</i><i>D</i><i>F</i>) test, is built on the basis of a composite null hypothesis. The <i>F</i><i>D</i><i>F</i> test can be applied to a sample generated from a type <i>I</i> or a type <i>I</i><i>I</i> fractional process. Depending on whether the test is applied to a sample generated from a type <i>I</i> or type <i>I</i><i>I</i> process, it is referred to as a test with or without prehistoric influence, respectively. The primary goal of this research is to investigate the effect of a pre-sample on the finite sample null distribution. The second objective is to highlight the theoretical justifications for the choice of the null composite hypothesis. All the theoretical results are illustrated with simulated and real data sets. Furthermore, to facilitate the reproducibility of our simulation data and figures, we provide all the necessary supplementary material consisting of EViews programs.
DOI: 10.6084/m9.figshare.28938901.v1
title: Fractional Dickey-Fuller test with or without prehistorical influence

description: Recently, the generalization of the standard Dickey-Fuller test to the fractional case has been proposed. This test, called the fractional Dickey-Fuller (<i>F</i><i>D</i><i>F</i>) test, is built on the basis of a composite null hypothesis. The <i>F</i><i>D</i><i>F</i> test can be applied to a sample generated from a type <i>I</i> or a type <i>I</i><i>I</i> fractional process. Depending on whether the test is applied to a sample generated from a type <i>I</i> or type <i>I</i><i>I</i> process, it is referred to as a test with or without prehistoric influence, respectively. The primary goal of this research is to investigate the effect of a pre-sample on the finite sample null distribution. The second objective is to highlight the theoretical justifications for the choice of the null composite hypothesis. All the theoretical results are illustrated with simulated and real data sets. Furthermore, to facilitate the reproducibility of our simulation data and figures, we provide all the necessary supplementary material consisting of EViews programs.
DOI: 10.6084/m9.figshare.28210271
title: Registration and estimation for functional generalized linear model

description: Functional data analysis has recently concentrated on the challenges of registration and estimation. Our research team has developed an innovative model that enables the simultaneous registration and estimation of multivariate functional data. This approach eliminates the need for pre-smoothing, thereby improving computational efficiency. We utilize the inverse warping function in conjunction with generalized functional principal component analysis (GFPCA) to approximate the covariance structure effectively. The inverse warping function, population mean function, and orthonormal eigenfunctions are estimated using B-spline methods. We have derived the joint probability distribution and established its asymptotic normality. Through extensive simulation studies and real data analyses, we compare our method with existing approaches and demonstrate its exceptional performance, showcasing superior fitting capabilities.
DOI: 10.6084/m9.figshare.28210271.v1
title: Registration and estimation for functional generalized linear model

description: Functional data analysis has recently concentrated on the challenges of registration and estimation. Our research team has developed an innovative model that enables the simultaneous registration and estimation of multivariate functional data. This approach eliminates the need for pre-smoothing, thereby improving computational efficiency. We utilize the inverse warping function in conjunction with generalized functional principal component analysis (GFPCA) to approximate the covariance structure effectively. The inverse warping function, population mean function, and orthonormal eigenfunctions are estimated using B-spline methods. We have derived the joint probability distribution and established its asymptotic normality. Through extensive simulation studies and real data analyses, we compare our method with existing approaches and demonstrate its exceptional performance, showcasing superior fitting capabilities.
DOI: 10.6084/m9.figshare.28521728
title: Forecasting based on a multivariate autoregressive threshold model (MTAR) with a multivariate Student’s t error distribution: A Bayesian approach

description: This article presents a Bayesian method for obtaining forecasts based on a multivariate threshold autoregressive (MTAR) model with a multivariate Student’s t error distribution. We derive the Bayesian predictive distribution, which incorporates uncertainty about the true values of the MTAR model parameters. The proposed procedure involves drawing samples from the predictive distribution to obtain point forecasts and prediction intervals for each univariate component of the multivariate process of interest. Although the model accounts for the multivariate nature of the data, the prediction intervals are constructed individually for each variable. We verify the performance of the proposed algorithm through a simulation study based on three models, calculating the percentage of times the true values of each component of the output process fall within the 95% individual prediction intervals of the predictive distribution. Additionally, we present an application to a set of financial time series, where forecasts of the returns of the Bovespa and Colcap indexes are obtained using the returns of the Standard and Poor’s 500 index as the threshold variable.
DOI: 10.6084/m9.figshare.28521728.v1
title: Forecasting based on a multivariate autoregressive threshold model (MTAR) with a multivariate Student’s t error distribution: A Bayesian approach

description: This article presents a Bayesian method for obtaining forecasts based on a multivariate threshold autoregressive (MTAR) model with a multivariate Student’s t error distribution. We derive the Bayesian predictive distribution, which incorporates uncertainty about the true values of the MTAR model parameters. The proposed procedure involves drawing samples from the predictive distribution to obtain point forecasts and prediction intervals for each univariate component of the multivariate process of interest. Although the model accounts for the multivariate nature of the data, the prediction intervals are constructed individually for each variable. We verify the performance of the proposed algorithm through a simulation study based on three models, calculating the percentage of times the true values of each component of the output process fall within the 95% individual prediction intervals of the predictive distribution. Additionally, we present an application to a set of financial time series, where forecasts of the returns of the Bovespa and Colcap indexes are obtained using the returns of the Standard and Poor’s 500 index as the threshold variable.
DOI: 10.6084/m9.figshare.29039425
title: Mixtures of generalized gamma convolution processes

description: A class of random measures derived from generalized gamma convolutions (GGC) is developed in the context of Bayesian statistics applications. These random measures can be represented as functionals of gamma processes, specifically as marginalized weighted gamma processes, offering greater flexibility compared to traditional gamma processes. Furthermore, they can be expressed in terms of a Dirichlet process, utilizing the approach used to represent weighted gamma processes as Dirichlet processes. Over the years, a variety of sampling strategies for Dirichlet process mixture models have been introduced, making it straightforward to sample posterior quantities derived from GGC processes. In this work, we derive Blackwell and MacQueen’s (1973) Pólya urn formula and the Chinese restaurant process from GGC processes. These results have direct applications in Bayesian mixture model estimation, and numerical simulations are provided for illustration.
DOI: 10.6084/m9.figshare.29039425.v1
title: Mixtures of generalized gamma convolution processes

description: A class of random measures derived from generalized gamma convolutions (GGC) is developed in the context of Bayesian statistics applications. These random measures can be represented as functionals of gamma processes, specifically as marginalized weighted gamma processes, offering greater flexibility compared to traditional gamma processes. Furthermore, they can be expressed in terms of a Dirichlet process, utilizing the approach used to represent weighted gamma processes as Dirichlet processes. Over the years, a variety of sampling strategies for Dirichlet process mixture models have been introduced, making it straightforward to sample posterior quantities derived from GGC processes. In this work, we derive Blackwell and MacQueen’s (1973) Pólya urn formula and the Chinese restaurant process from GGC processes. These results have direct applications in Bayesian mixture model estimation, and numerical simulations are provided for illustration.
DOI: 10.6084/m9.figshare.27715166
title: Propensity score matching for estimation of pairwise marginal hazard ratios

description: There is a growing interest in using observational studies to estimate the effects of treatments on survival or time-to-event outcomes. However, few standard approaches can adequately accommodate multiple treatment levels, which are common in observational comparative effectiveness research. We study the asymptotic properties of the generalized propensity score matching estimators of the marginal hazard ratios between pairs of treatment levels. The estimates are obtained by fitting a marginal Cox proportional hazard model on the matched dataset. We evaluate our approach in a simulation study and a case study where we analyze the IQVIA electronic medical records data.
DOI: 10.6084/m9.figshare.27715166.v3
title: Propensity score matching for estimation of pairwise marginal hazard ratios

description: There is a growing interest in using observational studies to estimate the effects of treatments on survival or time-to-event outcomes. However, few standard approaches can adequately accommodate multiple treatment levels, which are common in observational comparative effectiveness research. We study the asymptotic properties of the generalized propensity score matching estimators of the marginal hazard ratios between pairs of treatment levels. The estimates are obtained by fitting a marginal Cox proportional hazard model on the matched dataset. We evaluate our approach in a simulation study and a case study where we analyze the IQVIA electronic medical records data.
DOI: 10.6084/m9.figshare.27715166.v2
title: Propensity score matching for estimation of pairwise marginal hazard ratios

description: There is a growing interest in using observational studies to estimate the effects of treatments on survival or time-to-event outcomes. However, few standard approaches can adequately accommodate multiple treatment levels, which are common in observational comparative effectiveness research. We study the asymptotic properties of the generalized propensity score matching estimators of the marginal hazard ratios between pairs of treatment levels. The estimates are obtained by fitting a marginal Cox proportional hazard model on the matched dataset. We evaluate our approach in a simulation study and a case study where we analyze the IQVIA electronic medical records data.
DOI: 10.6084/m9.figshare.28581619
title: A new machine learning approach to optimize correlated biomarkers

description: The number of novel biomarkers is booming. However, a simple predictive score is more feasible to evaluate the clinical outcome and provide better accuracy. However, the optimal linear combination of correlated biomarkers demands comprehensive methodological research. This research aims to develop a novel approach for interpretable optimization. This research proposes the gradient boost machine with the Youden Index (GBYI) as the target function. The rationale is that the gradient boost machine demonstrates superior prediction ability and provides excellent interpretations according to the linear model. In addition, the Youden Index could effortlessly estimate the optimal cutoff point of the diagnostic test and evaluate the overall accuracy. Simulation studies evaluate the performance of the GBYI with linear and nonlinear structured datasets. We also demonstrate an application in the Bupa Liver Disease Data, which revealed that our optimal combination of correlated biomarkers shows an improved prediction with higher accuracy. This research proposes a novel machine-learning strategy using the powerful statistical boosting technique of the Youden Index. The new machine could optimize the combination of high-dimensional data and provide attractive interpretable coefficients.
DOI: 10.6084/m9.figshare.28581619.v1
title: A new machine learning approach to optimize correlated biomarkers

description: The number of novel biomarkers is booming. However, a simple predictive score is more feasible to evaluate the clinical outcome and provide better accuracy. However, the optimal linear combination of correlated biomarkers demands comprehensive methodological research. This research aims to develop a novel approach for interpretable optimization. This research proposes the gradient boost machine with the Youden Index (GBYI) as the target function. The rationale is that the gradient boost machine demonstrates superior prediction ability and provides excellent interpretations according to the linear model. In addition, the Youden Index could effortlessly estimate the optimal cutoff point of the diagnostic test and evaluate the overall accuracy. Simulation studies evaluate the performance of the GBYI with linear and nonlinear structured datasets. We also demonstrate an application in the Bupa Liver Disease Data, which revealed that our optimal combination of correlated biomarkers shows an improved prediction with higher accuracy. This research proposes a novel machine-learning strategy using the powerful statistical boosting technique of the Youden Index. The new machine could optimize the combination of high-dimensional data and provide attractive interpretable coefficients.
DOI: 10.6084/m9.figshare.31112506
title: Dataset excel

description: The exploratory study aimed to investigate whether users’ AI literacy is associated with the explanations they provide for errors made by the ChatGPT chatbot.
DOI: 10.6084/m9.figshare.31112506.v1
title: Dataset excel

description: The exploratory study aimed to investigate whether users’ AI literacy is associated with the explanations they provide for errors made by the ChatGPT chatbot.
DOI: 10.15468/dl.3z34y2
title: Occurrence Download

description: A dataset containing 2752 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Neovison vison",
 "Year 2016-2026"
 ]
}

 The dataset includes 2752 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002431-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x6q7rt
title: Occurrence Download

description: A dataset containing 169 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-73.342 4.185163, -73.412449 4.179593, -73.481161 4.163021, -73.54644 4.135856, -73.606679 4.098767, -73.660392 4.052669, -73.706256 3.998698, -73.74314200000001 3.938185, -73.770145 3.872621, -73.78660000000001 3.803621, -73.792106 3.732884, -73.786529 3.662154, -73.77001 3.59317, -73.74295600000001 3.527631, -73.70603699999999 3.467149, -73.660161 3.413213, -73.60646 3.36715, -73.546254 3.330093, -73.481025 3.302952, -73.412378 3.286397, -73.342 3.280832, -73.27162199999999 3.286397, -73.202975 3.302952, -73.13774600000001 3.330093, -73.07754 3.36715, -73.023839 3.413213, -72.977963 3.467149, -72.94104400000001 3.527631, -72.91399 3.59317, -72.897471 3.662154, -72.89189399999999 3.732884, -72.8974 3.803621, -72.913855 3.872621, -72.94085800000001 3.938185, -72.977744 3.998698, -73.023608 4.052669, -73.077321 4.098767, -73.13755999999999 4.135856, -73.202839 4.163021, -73.271551 4.179593, -73.342 4.185163))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 169 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002432-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18323880
title: Beyond the Throne – Worship in the Eternal Day – Tamil Christian Worship Album 7 (Lyrics & Videos)

description: Album 7: Beyond the Throne – Worship in the Eternal Day  சிங்காசனத்திற்கு அப்பால் – நித்திய நாளில் ஆராதனை

6 freshly anointed Tamil Christian worship songs with English translations.

Songs



பரிசுத்த பர்வதமான எனது கர்த்தாவே – O Holy Mountain, My Lord

நீதியின் தேவனே – God of Righteousness

என் அழுகையின் சத்தம் கேட்ப்பார் – He Hears My Cry

செம்மையான இருதயத்தை தாருமே – Grant Me a Pure Heart

இரவும் பகலும் வேதத்தை தியானிக்கும் மனிதன் பாக்கியவான் – Blessed Is the One Who Meditates Day and Night

வானத்திற்கு மேலாக உமது மகத்துவம் – Your Majesty Above the Heavens


This album is pure throne-centered worship — beyond time, beyond the tribulation, beyond the millennium — in the eternal day where God and the Lamb are the temple, the light, the river, and the life (Revelation 21–22).

Music generated with AI tools.  Lyrics original, written under divine inspiration.  All glory to the Lord Jesus Christ.

These songs are offered freely as worship to Jesus Christ and encouragement to the remnant.

இந்த பாடல்கள் இயேசு கிறிஸ்துவுக்கு ஆராதனையாகவும், எஞ்சியோருக்கு ஊக்கமாகவும் இலவசமாக வழங்கப்படுகின்றன.

Companion to Albums 1–6 and the Divine Neural Network revelation.

ஆமென் | அல்லேலூயா
DOI: 10.5281/zenodo.18323881
title: Beyond the Throne – Worship in the Eternal Day – Tamil Christian Worship Album 7 (Lyrics & Videos)

description: Album 7: Beyond the Throne – Worship in the Eternal Day  சிங்காசனத்திற்கு அப்பால் – நித்திய நாளில் ஆராதனை

6 freshly anointed Tamil Christian worship songs with English translations.

Songs



பரிசுத்த பர்வதமான எனது கர்த்தாவே – O Holy Mountain, My Lord

நீதியின் தேவனே – God of Righteousness

என் அழுகையின் சத்தம் கேட்ப்பார் – He Hears My Cry

செம்மையான இருதயத்தை தாருமே – Grant Me a Pure Heart

இரவும் பகலும் வேதத்தை தியானிக்கும் மனிதன் பாக்கியவான் – Blessed Is the One Who Meditates Day and Night

வானத்திற்கு மேலாக உமது மகத்துவம் – Your Majesty Above the Heavens


This album is pure throne-centered worship — beyond time, beyond the tribulation, beyond the millennium — in the eternal day where God and the Lamb are the temple, the light, the river, and the life (Revelation 21–22).

Music generated with AI tools.  Lyrics original, written under divine inspiration.  All glory to the Lord Jesus Christ.

These songs are offered freely as worship to Jesus Christ and encouragement to the remnant.

இந்த பாடல்கள் இயேசு கிறிஸ்துவுக்கு ஆராதனையாகவும், எஞ்சியோருக்கு ஊக்கமாகவும் இலவசமாக வழங்கப்படுகின்றன.

Companion to Albums 1–6 and the Divine Neural Network revelation.

ஆமென் | அல்லேலூயா
DOI: 10.6084/m9.figshare.31109302
title: Prediction of suitable distribution for typical vegetation restoration species in the QTP

description: This database stores occurrence records for six typical vegetation restoration species on the QTP （<i>Poa pratensis</i>, <i>Poa araratica</i>, <i>Poa pratensis var. anceps</i>, <i>Festuca sinensis</i>, <i>Elymus nutans</i>, and <i>Lolium perenne），</i>as well as spatially thinned occurrence points and a QTP boundary mask file.
DOI: 10.6084/m9.figshare.31109302.v1
title: Prediction of suitable distribution for typical vegetation restoration species in the QTP

description: This database stores occurrence records for six typical vegetation restoration species on the QTP （<i>Poa pratensis</i>, <i>Poa araratica</i>, <i>Poa pratensis var. anceps</i>, <i>Festuca sinensis</i>, <i>Elymus nutans</i>, and <i>Lolium perenne），</i>as well as spatially thinned occurrence points and a QTP boundary mask file.
DOI: 10.21227/vtqf-5j10
title: "Time-Series Dataset of Three-Phase Voltage, Current, and Power Parameters for Power Quality Analysis in Distribution Systems"

description: "This dataset presents high-resolution, time-synchronized three-phase electrical measurements obtained from a simulated distribution system model developed using MATLAB\/Simulink. The dataset includes supply-side and load-side three-phase voltage and current signals, along with derived electrical quantities such as active power, reactive power, and power factor. The time-series data are recorded with fine temporal resolution to accurately capture both steady-state and dynamic operating conditions commonly encountered in modern power distribution networks.The dataset is intended to support research in power quality assessment, voltage stability analysis, reactive power compensation, and distributed energy resource integration. It can be effectively used for analyzing voltage deviations, current imbalance, power factor variation, and transient behaviors under different operating scenarios. In addition, the availability of synchronized multi-parameter measurements makes the dataset suitable for advanced signal processing tasks, including feature extraction, statistical analysis, and harmonic or disturbance characterization.Owing to its structured and comprehensive nature, the dataset is particularly relevant for data-driven and machine learning\u2013based applications, such as power quality event classification, anomaly detection, predictive modeling, and intelligent control strategies involving controllers, energy storage systems, or compensating devices. The dataset is provided in a reusable format to facilitate reproducibility and comparative evaluation across different analytical and machine learning frameworks in power and energy systems research."