cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1604933
title: Evaluating the Calibration of Multi-Step-Ahead Density Forecasts Using Raw Moments

description: The evaluation of multi-step-ahead density forecasts is complicated by the serial correlation of the corresponding probability integral transforms. In the literature, three testing approaches can be found that take this problem into account. However, these approaches rely on data-dependent critical values, ignore important information and, therefore lack power, or suffer from size distortions even asymptotically. This article proposes a new testing approach based on raw moments. It is extremely easy to implement, uses standard critical values, can include all moments regarded as important, and has correct asymptotic size. It is found to have good size and power properties in finite samples if it is based on the (standardized) probability integral transforms.
DOI: 10.6084/m9.figshare.1604934
title: Inference in High-Dimensional Panel Models With an Application to Gun Control

description: We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high-dimensional setting. The setting allows the number of time-varying regressors to be larger than the sample size. To make informative estimation and inference feasible, we require that the overall contribution of the time-varying variables after eliminating the individual specific heterogeneity can be captured by a relatively small number of the available variables whose identities are unknown. This restriction allows the problem of estimation to proceed as a variable selection problem. Importantly, we treat the individual specific heterogeneity as fixed effects which allows this heterogeneity to be related to the observed time-varying variables in an unspecified way and allows that this heterogeneity may differ for all individuals. Within this framework, we provide procedures that give uniformly valid inference over a fixed subset of parameters in the canonical linear fixed effects model and over coefficients on a fixed vector of endogenous variables in panel data instrumental variable models with fixed effects and many instruments. We present simulation results in support of the theoretical developments and illustrate the use of the methods in an application aimed at estimating the effect of gun prevalence on crime rates.
DOI: 10.6084/m9.figshare.1604933.v1
title: Evaluating the Calibration of Multi-Step-Ahead Density Forecasts Using Raw Moments

description: The evaluation of multi-step-ahead density forecasts is complicated by the serial correlation of the corresponding probability integral transforms. In the literature, three testing approaches can be found that take this problem into account. However, these approaches rely on data-dependent critical values, ignore important information and, therefore lack power, or suffer from size distortions even asymptotically. This article proposes a new testing approach based on raw moments. It is extremely easy to implement, uses standard critical values, can include all moments regarded as important, and has correct asymptotic size. It is found to have good size and power properties in finite samples if it is based on the (standardized) probability integral transforms.
DOI: 10.6084/m9.figshare.1604934.v2
title: Inference in High-Dimensional Panel Models With an Application to Gun Control

description: We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high-dimensional setting. The setting allows the number of time-varying regressors to be larger than the sample size. To make informative estimation and inference feasible, we require that the overall contribution of the time-varying variables after eliminating the individual specific heterogeneity can be captured by a relatively small number of the available variables whose identities are unknown. This restriction allows the problem of estimation to proceed as a variable selection problem. Importantly, we treat the individual specific heterogeneity as fixed effects which allows this heterogeneity to be related to the observed time-varying variables in an unspecified way and allows that this heterogeneity may differ for all individuals. Within this framework, we provide procedures that give uniformly valid inference over a fixed subset of parameters in the canonical linear fixed effects model and over coefficients on a fixed vector of endogenous variables in panel data instrumental variable models with fixed effects and many instruments. We present simulation results in support of the theoretical developments and illustrate the use of the methods in an application aimed at estimating the effect of gun prevalence on crime rates.
DOI: 10.6084/m9.figshare.1604937.v2
title: Insights into the Neotropics prior to the Great American Biotic Interchange: new evidence of mammalian predators from the Miocene of Northern Colombia

description: A new species of Sparassodonta (Mammalia, Metatheria), <i>Lycopsis padillai</i>, sp. nov., is described on the basis of a partial left maxilla with M1–M4 and fragments of lacrimal and jugal. The material comes from the early to early middle Miocene Castilletes Formation, La Guajira Peninsula, Colombia. This specimen represents the northernmost record of a fossil metatherian in South America and integrates a highly diverse vertebrate association, recently discovered in the north of Colombia. The La Guajira specimen is referred to the genus <i>Lycopsis</i>, as supported by the results of our phylogenetic analysis. This analysis also demonstrates that species of <i>Lycopsis</i> (<i>L. torresi</i>, <i>L. longirostrus</i>, <i>L. viverensis</i>, and <i>L. padillai</i>) constitute a monophyletic group and are placed as the basal taxon of Borhyaenoidea. <i>Lycopsis padillai</i> is a large-sized sparassodont with a body mass of about 22 kg. The presence of <i>Lycopsis</i> from La Guajira extends the geographical distribution of the genus to the entire South America, representing the sparassodont with the widest latitudinal distribution. http://zoobank.org/urn:lsid:zoobank.org:pub:B3127EDAB4BB-4972-81B9-4447CC44EE79 SUPPLEMENTAL DATA—Supplemental materials are available for this article for free at www.tandfonline.com/UJVP Citation for this article: Suarez, C., A. M. Forasiepi, F. J. Goin, and C. Jaramillo. 2015. Insights into the Neotropics prior to the Great American Biotic Interchange: new evidence of mammalian predators from the Miocene of Northern Colombia. Journal of Vertebrate Paleontology. DOI: 10.1080/02724634.2015.1029581.
DOI: 10.6084/m9.figshare.1604936.v1
title: An augmented ADMM algorithm with application to the generalized lasso problem

description: In this article, we present a fast and stable algorithm for solving a class of optimization problems that arise in many statistical estimation procedures, such as <i>sparse fused lasso over a graph</i>, <i>convex clustering</i>, and <i>trend filtering</i>, among others. We propose a so-called augmented <i>alternating direction methods of multipliers</i> (ADMM) algorithm to solve this class of problems. Compared to a standard ADMM algorithm, our proposal significantly reduces the computational cost at each iteration while maintaining roughly the same overall convergence speed. We also consider a new varying penalty scheme for the ADMM algorithm, which could further accelerate the convergence, especially when solving a sequence of problems with tuning parameters of different scales. Extensive numerical experiments on the sparse fused lasso problem show that the proposed algorithm is more efficient than the standard ADMM and two other existing state-of-the-art specialized algorithms. Finally, we discuss a possible extension and some interesting connections to two well-known algorithms. Supplemental materials for the article are available online.
DOI: 10.6084/m9.figshare.1604934.v1
title: Inference in High Dimensional Panel Models with an Application to Gun Control

description: We consider estimation and inference in panel data models with additive unobserved individual specific heterogeneity in a high dimensional setting. The setting allows the number of time varying regressors to be larger than the sample size. To make informative estimation and inference feasible, we require that the overall contribution of the time varying variables after eliminating the individual specific heterogeneity can be captured by a relatively small number of the available variables whose identities are unknown. This restriction allows the problem of estimation to proceed as a variable selection problem. Importantly, we treat the individual specific heterogeneity as fixed effects which allows this heterogeneity to be related to the observed time varying variables in an unspecified way and allows that this heterogeneity may be non-zero for all individuals. Within this framework, we provide procedures that give uniformly valid inference over a fixed subset of parameters in the canonical linear fixed effects model and over coefficients on a fixed vector of endogenous variables in panel data instrumental variables models with fixed effects and many instruments. We present simulation results in support of the theoretical developments and illustrate the use of the methods in an application aimed at estimating the effect of gun prevalence on crime rates.
DOI: 10.6084/m9.figshare.1604937.v1
title: Insights into the Neotropics Prior to the Great American Biotic Interchange: new evidence of mammalian predators from the Miocene of Northern Colombia

description: ABSTRACTA new species of Sparassodonta (Mammalia, Metatheria), <i>Lycopsis padillai</i>, sp. nov., is described on the basis of a partial left maxilla with M1–M4 and fragments of lacrimal and jugal. The material comes from the early to early middle Miocene Castilletes Formation, La Guajira Peninsula, Colombia. This specimen represents the northernmost record of a fossil metatherian in South America and integrates a highly diverse vertebrate association, recently discovered in the north of Colombia. The La Guajira specimen is referred to the genus <i>Lycopsis</i>, as supported by the results of our phylogenetic analysis. This analysis also demonstrates that species of <i>Lycopsis</i> (<i>L. torresi</i>, <i>L. longirostrus</i>, <i>L. viverensis</i>, and <i>L. padillai</i>) constitute a monophyletic group and are placed as the basal taxon of Borhyaenoidea. <i>Lycopsis padillai</i> is a large-sized sparassodont with a body mass of about 22 kg. The presence of <i>Lycopsis</i> from La Guajira extends the geographical distribution of the genus to the entire South America, representing the sparassodont with the widest latitudinal distribution.http://zoobank.org/urn:lsid:zoobank.org:pub:B3127EDAB4BB-4972-81B9-4447CC44EE79SUPPLEMENTAL DATA—Supplemental materials are available for this article for free at www.tandfonline.com/UJVPCitation for this article: Suarez, C., A. M. Forasiepi, F. J. Goin, and C. Jaramillo. 2015. Insights into the Neotropics prior to the Great American Biotic Interchange: new evidence of mammalian predators from the Miocene of Northern Colombia. Journal of Vertebrate Paleontology. DOI: 10.1080/02724634.2015.1029581.
DOI: 10.6084/m9.figshare.1604931
title: WEHI_3groups_cytokines

description: Demo data 3 groups
DOI: 10.6084/m9.figshare.1604918
title: Machine Learning for Biology at ANU

description: Machine Learning for Biology day at ANU on 13/11/15
DOI: 10.6084/m9.figshare.1604931.v1
title: WEHI_3groups_cytokines

description: Demo data 3 groups
DOI: 10.6084/m9.figshare.1604917
title: Practical Advice for Debugging Machine Learning Algorithms: Steve Gould

description: Lecture by Steve Gould about debugging machine learning algorithms presented at the Machine Learning for Biology Day at ANU.
DOI: 10.6084/m9.figshare.1604915
title: Patterns of, and Factors Associated With, Illicit Pharmaceutical Opioid Analgesic Use in a Prospective Cohort of People Who Inject Drugs in Melbourne, Australia

description: <b><i>Background:</i></b> People who inject drugs (PWID) are a key population engaging in pharmaceutical opioid analgesic (PO) use, yet little is known about patterns of illicit PO use among this group. <b><i>Objectives:</i></b> The aims of this research were to measure the prevalence and frequency of lifetime and past-month illicit PO use and injection in a sample of regular PWID, to examine patterns of past-month illicit PO use within individuals over time, and to identify factors independently associated with past-month illicit PO use. <b><i>Methods:</i></b> Data were drawn from a prospective cohort study of regular PWID (<i>N</i> = 666) in Melbourne, Australia. Data from five waves of annual data collection (including baseline) were analyzed descriptively and using generalized estimating equations (GEE). <b><i>Results:</i></b> At baseline, 59% of participants reported lifetime illicit PO use and 20% reported past-month use, predominantly through injecting. Most illicit PO users at baseline transitioned to nonuse of illicit POs across the study period. In multivariable GEE analysis, factors associated with past-month illicit PO use included past-year arrest [adjusted odds ratio (AOR): 1.39], opioids other than heroin as drug of choice (AOR: 5.14), experiencing poorer physical health (AOR: 0.98) and a range of other drug use variables. <b><i>Conclusions:</i></b> We found little evidence of ongoing illicit PO use among those followed up, with illicit PO use linked to polydrug use more broadly. Nonetheless, trends in illicit PO use among PWID should continue to be monitored and harm reduction interventions implemented to reduce the associated public health risks.
DOI: 10.6084/m9.figshare.1604916
title: Types of Machine Learning Problems: Mark Reid

description: Lecture by Mark Reid about the Types of Machine Learning Problems, presented at the Machine Learning for Biology Day at ANU.
DOI: 10.6084/m9.figshare.1604914
title: WEHI_R_Demo

description: demo data for R demonstration
DOI: 10.6084/m9.figshare.1604916.v2
title: Types of Machine Learning Problems: Mark Reid

description: Lecture by Mark Reid about the Types of Machine Learning Problems, presented at the Machine Learning for Biology Day at ANU.
DOI: 10.6084/m9.figshare.1604917.v2
title: Practical Advice for Debugging Machine Learning Algorithms: Steve Gould

description: Lecture by Steve Gould about debugging machine learning algorithms presented at the Machine Learning for Biology Day at ANU.
DOI: 10.6084/m9.figshare.1604918.v1
title: new fileset

description: Machine Learning for Biology day at ANU on 13/11/15
DOI: 10.6084/m9.figshare.1604914.v1
title: WEHI_R_Demo

description: demo data for R demonstration
DOI: 10.6084/m9.figshare.1604916.v1
title: Types of Machine Learning Problems

description: Lecture by Mark Reid about the Types of Machine Learning Problems, presented at the Machine Learning for Biology Day at ANU.
DOI: 10.6084/m9.figshare.1604917.v1
title: Practical Advice for Debugging Machine Learning Algorithms

description: Lecture by Steve Gould about debugging machine learning algorithms presented at the Machine Learning for Biology Day at ANU.
DOI: 10.6084/m9.figshare.1604915.v2
title: Patterns of, and Factors Associated With, Illicit Pharmaceutical Opioid Analgesic Use in a Prospective Cohort of People Who Inject Drugs in Melbourne, Australia

description: <b><i>Background:</i></b> People who inject drugs (PWID) are a key population engaging in pharmaceutical opioid analgesic (PO) use, yet little is known about patterns of illicit PO use among this group. <b><i>Objectives:</i></b> The aims of this research were to measure the prevalence and frequency of lifetime and past-month illicit PO use and injection in a sample of regular PWID, to examine patterns of past-month illicit PO use within individuals over time, and to identify factors independently associated with past-month illicit PO use. <b><i>Methods:</i></b> Data were drawn from a prospective cohort study of regular PWID (<i>N</i> = 666) in Melbourne, Australia. Data from five waves of annual data collection (including baseline) were analyzed descriptively and using generalized estimating equations (GEE). <b><i>Results:</i></b> At baseline, 59% of participants reported lifetime illicit PO use and 20% reported past-month use, predominantly through injecting. Most illicit PO users at baseline transitioned to nonuse of illicit POs across the study period. In multivariable GEE analysis, factors associated with past-month illicit PO use included past-year arrest [adjusted odds ratio (AOR): 1.39], opioids other than heroin as drug of choice (AOR: 5.14), experiencing poorer physical health (AOR: 0.98) and a range of other drug use variables. <b><i>Conclusions:</i></b> We found little evidence of ongoing illicit PO use among those followed up, with illicit PO use linked to polydrug use more broadly. Nonetheless, trends in illicit PO use among PWID should continue to be monitored and harm reduction interventions implemented to reduce the associated public health risks.
DOI: 10.6084/m9.figshare.1604907
title: Supplementary_Zip_Files_S2.1.zip

description: These are supporting information related to PhD thesis. All rights reserved © 2015 Pallab Bhowmick Apart from any fair dealing for the purposes of research, private study, criticism or review, this publication may not be reproduced, stored in a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, scanning, or otherwise), without prior written permission of the author.
DOI: 10.6084/m9.figshare.1604915.v1
title: Patterns of, and Factors Associated With, Illicit Pharmaceutical Opioid Analgesic Use in a Prospective Cohort of People Who Inject Drugs in Melbourne, Australia

description: <b><i>Background:</i></b> People who inject drugs (PWID) are a key population engaging in pharmaceutical opioid analgesic (PO) use, yet little is known about patterns of illicit PO use among this group. <b><i>Objectives:</i></b> The aims of this research were to measure the prevalence and frequency of lifetime and past-month illicit PO use and injection in a sample of regular PWID, to examine patterns of past-month illicit PO use within individuals over time, and to identify factors independently associated with past-month illicit PO use. <b><i>Methods:</i></b> Data were drawn from a prospective cohort study of regular PWID (<i>N</i> = 666) in Melbourne, Australia. Data from five waves of annual data collection (including baseline) were analyzed descriptively and using generalized estimating equations (GEE). <b><i>Results:</i></b> At baseline, 59% of participants reported lifetime illicit PO use and 20% reported past-month use, predominantly through injecting. Most illicit PO users at baseline transitioned to nonuse of illicit POs across the study period. In multivariable GEE analysis, factors associated with past-month illicit PO use included past-year arrest [adjusted odds ratio (AOR): 1.39], opioids other than heroin as drug of choice (AOR: 5.14), experiencing poorer physical health (AOR: 0.98) and a range of other drug use variables. <b><i>Conclusions:</i></b> We found little evidence of ongoing illicit PO use among those followed up, with illicit PO use linked to polydrug use more broadly. Nonetheless, trends in illicit PO use among PWID should continue to be monitored and harm reduction interventions implemented to reduce the associated public health risks.
DOI: 10.6084/m9.figshare.1604918.v2
title: Machine Learning for Biology at ANU

description: Machine Learning for Biology day at ANU on 13/11/15
DOI: 10.6084/m9.figshare.1604904
title: Supplementary Table 2.5.xls

description: These are supporting information related to PhD thesis. All rights reserved © 2015 Pallab Bhowmick Apart from any fair dealing for the purposes of research, private study, criticism or review, this publication may not be reproduced, stored in a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, scanning, or otherwise), without prior written permission of the author.
DOI: 10.6084/m9.figshare.1604918.v4
title: Machine Learning for Biology at ANU

description: Machine Learning for Biology day at ANU on 13/11/15
DOI: 10.6084/m9.figshare.1604907.v1
title: Supplementary_Zip_Files_S2.1.zip

description: These are supporting information related to PhD thesis. All rights reserved © 2015 Pallab Bhowmick Apart from any fair dealing for the purposes of research, private study, criticism or review, this publication may not be reproduced, stored in a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, scanning, or otherwise), without prior written permission of the author.
DOI: 10.6084/m9.figshare.1604903
title: Supplementary Table 2.3.xls

description: These are supporting information related to PhD thesis. All rights reserved © 2015 Pallab Bhowmick Apart from any fair dealing for the purposes of research, private study, criticism or review, this publication may not be reproduced, stored in a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, scanning, or otherwise), without prior written permission of the author.
DOI: 10.6084/m9.figshare.1604918.v3
title: Machine Learning for Biology at ANU

description: Machine Learning for Biology day at ANU on 13/11/15
DOI: 10.6084/m9.figshare.1604902
title: Supplementary Table 3.11.xlsx

description: These are supporting information related to PhD thesis. All rights reserved © 2015 Pallab Bhowmick Apart from any fair dealing for the purposes of research, private study, criticism or review, this publication may not be reproduced, stored in a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, scanning, or otherwise), without prior written permission of the author.
DOI: 10.6084/m9.figshare.1604903.v1
title: Supplementary Table 2.3.xls

description: These are supporting information related to PhD thesis. All rights reserved © 2015 Pallab Bhowmick Apart from any fair dealing for the purposes of research, private study, criticism or review, this publication may not be reproduced, stored in a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, scanning, or otherwise), without prior written permission of the author.
DOI: 10.6084/m9.figshare.1604904.v1
title: Supplementary Table 2.5.xls

description: These are supporting information related to PhD thesis. All rights reserved © 2015 Pallab Bhowmick Apart from any fair dealing for the purposes of research, private study, criticism or review, this publication may not be reproduced, stored in a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, scanning, or otherwise), without prior written permission of the author.
DOI: 10.6084/m9.figshare.1604902.v1
title: Supplementary Table 3.11.xlsx

description: These are supporting information related to PhD thesis. All rights reserved © 2015 Pallab Bhowmick Apart from any fair dealing for the purposes of research, private study, criticism or review, this publication may not be reproduced, stored in a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, scanning, or otherwise), without prior written permission of the author.
DOI: 10.6084/m9.figshare.1604899
title: Supplementary Table 3.7.xlsx

description: These are supporting information related to PhD thesis. All rights reserved © 2015 Pallab Bhowmick Apart from any fair dealing for the purposes of research, private study, criticism or review, this publication may not be reproduced, stored in a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, scanning, or otherwise), without prior written permission of the author.