cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5536255
title: Eocene to Oligocene vegetation and climate in the Tasmanian Gateway region controlled by changes in ocean currents and pCO2

description: Datasets accompanying Eocene to Oligocene vegetation and climate in the Tasmanian Gateway region controlled by changes in ocean currents and pCO2 by Amoo et al. Supplementary table S1: Raw palynomorph assemblage data, total counts, ODP Site 1172 Supplementary table S2: Sporomorph-based climate estimates including MAT, WMMT, CMMT and MAP Supplementary table S3: Sporomorph diversity indices
DOI: 10.6084/m9.figshare.16695118.v3
title: Attraction of Choristoneura rosaceana (Lepidoptera: Tortricidae) to Pheromone Blends in Ratios Produced in Females’ Pheromone Gland or Emitted by the Females in Michigan Apple Orchards

description: Trap captures of obliquebanded leafroller, <i>Choristoneura rosaceana</i> (Harris) to pheromone blends in ratios approximating those reported in pheromone glands and a novel blend based on a volatile headspace collection from live virgin females were evaluated in field experiments in Michigan apple orchards. In an initial field trapping study, pheromone lures composed of either a three- or four-component blend approximation of the blend present in female pheromone glands at doses ranging from 0.1 to 20 mg/lure were compared. The four-component blend was a combination of (<i>Z</i>)-11-tetradecenyl acetate (<i>Z</i>11-14:Ac), (<i>E</i>)-11-tetradecenyl acetate (<i>E</i>11-14:Ac), (<i>Z</i>)-11-tetradecen-1-ol (<i>Z</i>11-14:OH), and (<i>Z</i>)-11-tetradecenal (<i>Z</i>11-14:Al) in a ratio of 96.5:1.8:1.4:0.2, respectively, while the three-component blend lacked <i>Z</i>11-14:Al. Pheromone emissions by groups of virgin females and commercial lures were collected in the laboratory and analyzed by gas chromatography. These data were used to formulate a new pheromone lure that was compared to a commercial lure in a second trapping study. In the first field study, traps baited with 10 mg pheromones or above captured significantly more moths than traps baited with 1 mg or less, regardless of the blend. Surprisingly, groups of virgin females only emitted two detectable pheromone components, <i>Z</i>11-14:Ac and <i>Z</i>11-14:OH in a ratio of 37:63 which was substantially different from the blends detected in pheromone glands in the literature. The newly formulated pheromone lure based on females’ emission was more than twice as attractive as the commercial lure which emitted a 74:5:21 three-component blend of <i>Z</i>11-14:Ac, <i>E</i>11-14:Ac, and <i>Z</i>11-14:OH. Our results suggested that the actual pheromones released by females may not translate directly to the content of pheromone glands.<b></b>
DOI: 10.6084/m9.figshare.16695118.v2
title: Attraction of Choristoneura rosaceana (Lepidoptera: Tortricidae) to Pheromone Blends in Ratios Produced in Females’ Pheromone Gland or Emitted by the Females in Michigan Apple Orchards

description: Trap captures of obliquebanded leafroller, <i>Choristoneura rosaceana</i> (Harris) to pheromone blends in ratios approximating those reported in pheromone glands and a novel blend based on a volatile headspace collection from live virgin females were evaluated in field experiments in Michigan apple orchards. In an initial field trapping study, pheromone lures composed of either a three- or four-component blend approximation of the blend present in female pheromone glands at doses ranging from 0.1 to 20 mg/lure were compared. The four-component blend was a combination of (<i>Z</i>)-11-tetradecenyl acetate (<i>Z</i>11-14:Ac), (<i>E</i>)-11-tetradecenyl acetate (<i>E</i>11-14:Ac), (<i>Z</i>)-11-tetradecen-1-ol (<i>Z</i>11-14:OH), and (<i>Z</i>)-11-tetradecenal (<i>Z</i>11-14:Al) in a ratio of 96.5:1.8:1.4:0.2, respectively, while the three-component blend lacked <i>Z</i>11-14:Al. Pheromone emissions by groups of virgin females and commercial lures were collected in the laboratory and analyzed by gas chromatography. These data were used to formulate a new pheromone lure that was compared to a commercial lure in a second trapping study. In the first field study, traps baited with 10 mg pheromones or above captured significantly more moths than traps baited with 1 mg or less, regardless of the blend. Surprisingly, groups of virgin females only emitted two detectable pheromone components, <i>Z</i>11-14:Ac and <i>Z</i>11-14:OH in a ratio of 37:63 which was substantially different from the blends detected in pheromone glands in the literature. The newly formulated pheromone lure based on females’ emission was more than twice as attractive as the commercial lure which emitted a 74:5:21 three-component blend of <i>Z</i>11-14:Ac, <i>E</i>11-14:Ac, and <i>Z</i>11-14:OH. Our results suggested that the actual pheromones released by females may not translate directly to the content of pheromone glands.<b></b>
DOI: 10.6073/pasta/3c13d83163859f517882e6376a037953
title: Morphological and life-history traits of birds from the Piedmont and Flooded Savannas of the Colombian Llanos

description: We compiled data for 13 morphological and life-history traits of 364 species reported in Colombian Llanos. The dataset includes morphological and life-history traits that could influence birds’ responses to land-use change based on a literature review. Life-history traits were compiled from scientific literature, while morphological traits were measured on specimens collected in the Colombian Llanos region and preserved in main Colombia’s biological collections.
DOI: 10.6084/m9.figshare.8218763
title: Causal Interpretations of Black-Box Models

description: The fields of machine learning and causal inference have developed many concepts, tools, and theory that are potentially useful for each other. Through exploring the possibility of extracting causal interpretations from black-box machine-trained models, we briefly review the languages and concepts in causal inference that may be interesting to machine learning researchers. We start with the curious observation that Friedman’s partial dependence plot has exactly the same formula as Pearl’s back-door adjustment and discuss three requirements to make causal interpretations: a model with good predictive performance, some domain knowledge in the form of a causal diagram and suitable visualization tools. We provide several illustrative examples and find some interesting and potentially causal relations using visualization tools for black-box models.
DOI: 10.6084/m9.figshare.8218763.v3
title: Causal Interpretations of Black-Box Models

description: The fields of machine learning and causal inference have developed many concepts, tools, and theory that are potentially useful for each other. Through exploring the possibility of extracting causal interpretations from black-box machine-trained models, we briefly review the languages and concepts in causal inference that may be interesting to machine learning researchers. We start with the curious observation that Friedman’s partial dependence plot has exactly the same formula as Pearl’s back-door adjustment and discuss three requirements to make causal interpretations: a model with good predictive performance, some domain knowledge in the form of a causal diagram and suitable visualization tools. We provide several illustrative examples and find some interesting and potentially causal relations using visualization tools for black-box models.
DOI: 10.6084/m9.figshare.8220689
title: Measurement Error Without the Proxy Exclusion Restriction

description: <b><i>Abstract–</i>This article studies the identification of the coefficients in a linear equation when data on the outcome, covariates, and an error-laden proxy for a latent variable are available. We maintain that the measurement error in the proxy is classical and relax the assumption that the proxy is excluded from the outcome equation. This enables the proxy to directly affect the outcome and allows for differential measurement error. Without the proxy exclusion restriction, we first show that the effects of the latent variable, the proxy, and the covariates are not identified. We then derive the sharp identification regions for these effects under any configuration of three auxiliary assumptions. The first weakens the assumption of no measurement error by imposing an upper bound on the noise-to-signal ratio. The second imposes an upper bound on the outcome equation coefficient of determination that would obtain had there been no measurement error. The third weakens the proxy exclusion restriction by specifying whether the latent variable and its proxy affect the outcome in the same or the opposite direction, if at all. Using the College Scorecard aggregate data, we illustrate our framework by studying the financial returns to college selectivity and characteristics and student characteristics when the average SAT score at an institution may directly affect earnings and serves as a proxy for the average ability of the student cohort.</b>
DOI: 10.6084/m9.figshare.9762530
title: Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method

description: We propose statistical inferential procedures for nonparametric panel data models with interactive fixed effects in a kernel ridge regression framework. Compared with the traditional sieve methods, our method is automatic in the sense that it does not require the choice of basis functions and truncation parameters. The model complexity is controlled by a continuous regularization parameter which can be automatically selected by the generalized cross-validation. Based on the empirical process theory and functional analysis tools, we derive the joint asymptotic distributions for the estimators in the heterogeneous setting. These joint asymptotic results are then used to construct the confidence intervals for the regression means and the prediction intervals for future observations, both being the first provably valid intervals in literature. The marginal asymptotic normality of the functional estimators in a homogeneous setting is also obtained. Our estimators can also be readily modified and applied to other widely used semiparametric models, such as partially linear models. Simulation and real data analyses demonstrate the advantages of our method. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8218760
title: A Dynamic Model of Vaccine Compliance: How Fake News Undermined the Danish HPV Vaccine Program

description: Increased vaccine hesitancy presents challenges to public health and undermines efforts to eradicate diseases such as measles, rubella, and polio. The decline is partly attributed to misconceptions that are shared on social media, such as the debunked association between vaccines and autism. Perhaps, more damaging to vaccine uptake are cases where trusted mainstream media run stories that exaggerate the risks associated with vaccines. It is important to understand the underlying causes of vaccine refusal, because these may be prevented, or countered, in a timely manner by educational campaigns. In this article, we develop a dynamic model of vaccine compliance that can help pinpoint events that disrupted vaccine compliance. We apply the framework to Danish HPV vaccine data, which experienced a sharp decline in compliance following the broadcast of a controversial TV documentary, and we show that media coverage significantly predicts vaccine uptake.
DOI: 10.6084/m9.figshare.8218760.v2
title: A Dynamic Model of Vaccine Compliance: How Fake News Undermined the Danish HPV Vaccine Program

description: Increased vaccine hesitancy presents challenges to public health and undermines efforts to eradicate diseases such as measles, rubella, and polio. The decline is partly attributed to misconceptions that are shared on social media, such as the debunked association between vaccines and autism. Perhaps, more damaging to vaccine uptake are cases where trusted mainstream media run stories that exaggerate the risks associated with vaccines. It is important to understand the underlying causes of vaccine refusal, because these may be prevented, or countered, in a timely manner by educational campaigns. In this article, we develop a dynamic model of vaccine compliance that can help pinpoint events that disrupted vaccine compliance. We apply the framework to Danish HPV vaccine data, which experienced a sharp decline in compliance following the broadcast of a controversial TV documentary, and we show that media coverage significantly predicts vaccine uptake.
DOI: 10.6084/m9.figshare.9762530.v3
title: Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method

description: We propose statistical inferential procedures for nonparametric panel data models with interactive fixed effects in a kernel ridge regression framework. Compared with the traditional sieve methods, our method is automatic in the sense that it does not require the choice of basis functions and truncation parameters. The model complexity is controlled by a continuous regularization parameter which can be automatically selected by the generalized cross-validation. Based on the empirical process theory and functional analysis tools, we derive the joint asymptotic distributions for the estimators in the heterogeneous setting. These joint asymptotic results are then used to construct the confidence intervals for the regression means and the prediction intervals for future observations, both being the first provably valid intervals in literature. The marginal asymptotic normality of the functional estimators in a homogeneous setting is also obtained. Our estimators can also be readily modified and applied to other widely used semiparametric models, such as partially linear models. Simulation and real data analyses demonstrate the advantages of our method. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8218763.v2
title: Causal Interpretations of Black-Box Models

description: The fields of machine learning and causal inference have developed many concepts, tools, and theory that are potentially useful for each other. Through exploring the possibility of extracting causal interpretations from black-box machine-trained models, we briefly review the languages and concepts in causal inference that may be interesting to machine learning researchers. We start with the curious observation that Friedman’s partial dependence plot has exactly the same formula as Pearl’s back-door adjustment and discuss three requirements to make causal interpretations: a model with good predictive performance, some domain knowledge in the form of a causal diagram and suitable visualization tools. We provide several illustrative examples and find some interesting and potentially causal relations using visualization tools for black-box models.
DOI: 10.6084/m9.figshare.8220689.v2
title: Measurement Error Without the Proxy Exclusion Restriction

description: This article studies the identification of the coefficients in a linear equation when data on the outcome, covariates, and an error-laden proxy for a latent variable are available. We maintain that the measurement error in the proxy is classical and relax the assumption that the proxy is excluded from the outcome equation. This enables the proxy to directly affect the outcome and allows for differential measurement error. Without the proxy exclusion restriction, we first show that the effects of the latent variable, the proxy, and the covariates are not identified. We then derive the sharp identification regions for these effects under any configuration of three auxiliary assumptions. The first weakens the assumption of no measurement error by imposing an upper bound on the noise-to-signal ratio. The second imposes an upper bound on the outcome equation coefficient of determination that would obtain had there been no measurement error. The third weakens the proxy exclusion restriction by specifying whether the latent variable and its proxy affect the outcome in the same or the opposite direction, if at all. Using the College Scorecard aggregate data, we illustrate our framework by studying the financial returns to college selectivity and characteristics and student characteristics when the average SAT score at an institution may directly affect earnings and serves as a proxy for the average ability of the student cohort.
DOI: 10.6084/m9.figshare.9762530.v2
title: Statistical Inference on Panel Data Models: A Kernel Ridge Regression Method

description: We propose statistical inferential procedures for nonparametric panel data models with interactive fixed effects in a kernel ridge regression framework. Compared with the traditional sieve methods, our method is automatic in the sense that it does not require the choice of basis functions and truncation parameters. The model complexity is controlled by a continuous regularization parameter which can be automatically selected by the generalized cross-validation. Based on the empirical process theory and functional analysis tools, we derive the joint asymptotic distributions for the estimators in the heterogeneous setting. These joint asymptotic results are then used to construct the confidence intervals for the regression means and the prediction intervals for future observations, both being the first provably valid intervals in literature. The marginal asymptotic normality of the functional estimators in a homogeneous setting is also obtained. Our estimators can also be readily modified and applied to other widely used semiparametric models, such as partially linear models. Simulation and real data analyses demonstrate the advantages of our method. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9677687
title: Testing for Changes in Forecasting Performance

description: We consider the issue of forecast failure (or breakdown) and propose methods to assess retrospectively whether a given forecasting model provides forecasts which show evidence of changes with respect to some loss function. We adapt the classical structural change tests to the forecast failure context. First, we recommend that all tests should be carried with a fixed scheme to have best power. This ensures a maximum difference between the fitted in and out-of-sample means of the losses and avoids contamination issues under the rolling and recursive schemes. With a fixed scheme, Giacomini and Rossi’s (GR) test is simply a Wald test for a one-time change in the mean of the total (the in-sample plus out-of-sample) losses at a known break date, say <i>m</i>, the value that separates the in and out-of-sample periods. To alleviate this problem, we consider a variety of tests: maximizing the GR test over values of <i>m</i> within a prespecified range; a Double sup-Wald (DSW) test which for each <i>m</i> performs a sup-Wald test for a change in the mean of the out-of-sample losses and takes the maximum of such tests over some range; we also propose to work directly with the total loss series to define the Total Loss sup-Wald and Total Loss UDmax (TLUD) tests. Using theoretical analyses and simulations, we show that with forecasting models potentially involving lagged dependent variables, the only tests having a monotonic power function for all data-generating processes considered are the DSW and TLUD tests, constructed with a fixed forecasting window scheme. Some explanations are provided and empirical applications illustrate the relevance of our findings in practice. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8940344
title: Multi-Horizon Forecast Comparison

description: We introduce tests for multi-horizon superior predictive ability (SPA). Rather than comparing forecasts of different models at multiple horizons individually, we propose to jointly consider all horizons of a forecast path. We define the concepts of uniform and average SPA. The former entails superior performance at each individual horizon, while the latter allows inferior performance at some horizons to be compensated by others. The article illustrates how the tests lead to more coherent conclusions, and how they are better able to differentiate between models than the single-horizon tests. We provide an extension of the previously introduced model confidence set to allow for multi-horizon comparison of more than two models. Simulations demonstrate appropriate size and high power. An illustration of the tests on a large set of macroeconomic variables demonstrates the empirical benefits of multi-horizon comparison.
DOI: 10.6084/m9.figshare.8940344.v2
title: Multi-Horizon Forecast Comparison

description: We introduce tests for multi-horizon superior predictive ability (SPA). Rather than comparing forecasts of different models at multiple horizons individually, we propose to jointly consider all horizons of a forecast path. We define the concepts of uniform and average SPA. The former entails superior performance at each individual horizon, while the latter allows inferior performance at some horizons to be compensated by others. The article illustrates how the tests lead to more coherent conclusions, and how they are better able to differentiate between models than the single-horizon tests. We provide an extension of the previously introduced model confidence set to allow for multi-horizon comparison of more than two models. Simulations demonstrate appropriate size and high power. An illustration of the tests on a large set of macroeconomic variables demonstrates the empirical benefits of multi-horizon comparison.
DOI: 10.6084/m9.figshare.9105017
title: Exploring Encouragement, Treatment, and Spillover Effects Using Principal Stratification, With Application to a Field Experiment on Teens’ Museum Attendance

description: This article revisits results from a field experiment, conducted in Florence, Italy, to study the effects of incentives provided to high school teens to motivate them to visit art museums. In the experiment, different classes of students were randomized to three types of encouragement and were offered a free visit to a main museum in the city. Using the principal stratification framework, the article explores causal pathways that may lead students to increase future visits, as induced by the encouragement received, or by the individual experience of the proposed free museum visit, or by the spillover of classmates’ experience. We do so by estimating and interpreting the causal effects of the three forms of encouragement within the principal strata defined by compliance behaviors. Bayesian inferential methods are used to derive the posterior distributions of weakly identified causal parameters.
DOI: 10.6084/m9.figshare.9105017.v3
title: Exploring Encouragement, Treatment, and Spillover Effects Using Principal Stratification, With Application to a Field Experiment on Teens’ Museum Attendance

description: This article revisits results from a field experiment, conducted in Florence, Italy, to study the effects of incentives provided to high school teens to motivate them to visit art museums. In the experiment, different classes of students were randomized to three types of encouragement and were offered a free visit to a main museum in the city. Using the principal stratification framework, the article explores causal pathways that may lead students to increase future visits, as induced by the encouragement received, or by the individual experience of the proposed free museum visit, or by the spillover of classmates’ experience. We do so by estimating and interpreting the causal effects of the three forms of encouragement within the principal strata defined by compliance behaviors. Bayesian inferential methods are used to derive the posterior distributions of weakly identified causal parameters.