Subjects -> AGRICULTURE (Total: 981 journals)     - AGRICULTURAL ECONOMICS (93 journals)    - AGRICULTURE (680 journals)    - CROP PRODUCTION AND SOIL (120 journals)    - DAIRYING AND DAIRY PRODUCTS (30 journals)    - POULTRY AND LIVESTOCK (58 journals) AGRICULTURE (680 journals)            First | 1 2 3 4
 Showing 201 - 263 of 263 Journals sorted alphabetically Economic Affairs       (Followers: 8) Economic and Industrial Democracy       (Followers: 12) Economic Bulletin       (Followers: 6) Economic Policy       (Followers: 51) Economic Record       (Followers: 7) Economics of Disasters and Climate Change       (Followers: 14) Egyptian Journal of Biological Pest Control Emirates Journal of Food and Agriculture       (Followers: 1) Empirical Economics       (Followers: 16) Encuentro Energy Nexus Ensaios e Ciência : Ciências Biológicas, Agrárias e da Saúde Environment and Development Economics       (Followers: 43) Eppo Bulletin       (Followers: 1) Ethiopian Journal of Agricultural Sciences Ethiopian Journal of Science and Technology Ethiopian Journal of Sciences and Sustainable Development Ethology       (Followers: 11) EU Agrarian Law       (Followers: 4) Euphytica       (Followers: 8) Eurochoices       (Followers: 1) European Journal of Agronomy       (Followers: 12) European Journal of American Culture European Journal of Health Economics       (Followers: 24) European Journal of Law and Economics       (Followers: 50) European Review of Agricultural Economics       (Followers: 12) EvoDevo       (Followers: 4) Farm Engineering and Automation Technology Journal Fave : Sección ciencias agrarias Fitosanidad Florea : Jurnal Biologi dan Pembelajarannya Folia Horticulturae       (Followers: 1) Folia Oecologica Food and Agricultural Immunology       (Followers: 2) Food and Ecological Systems Modelling Journal Food and Energy Security       (Followers: 3) Food and Environment Safety       (Followers: 3) Food Biotechnology       (Followers: 8) Food Economics - Acta Agriculturae Scandinavica, Section C       (Followers: 1) Food New Zealand       (Followers: 1) Food Policy       (Followers: 36) Forest@ : Journal of Silviculture and Forest Ecology Forestry Chronicle       (Followers: 9) Fronteiras : Journal of Social, Technological and Environmental Science Frontiers in Science       (Followers: 1) Fundamental and Applied Agriculture Future Foods       (Followers: 3) Future of Food : Journal on Food, Agriculture and Society       (Followers: 19) Gema Agro Geoderma       (Followers: 8) Ghana Journal of Agricultural Science       (Followers: 2) Global Approaches to Extension Practice : A Journal of Agricultural Extension       (Followers: 1) Global Economic Review       (Followers: 8) Global Journal of Agricultural Sciences Global Journal of Biology, Agriculture & Health Sciences Gontor Agrotech Science Journal Hacquetia Health Economics, Policy and Law       (Followers: 26) Heliyon Hereditas       (Followers: 1) Horticultural Studies Huria : Journal of the Open University of Tanzania       (Followers: 2) IDESIA : Revista de Agricultura en Zonas Áridas Indian Horticulture       (Followers: 8) Indian Journal of Agricultural Sciences       (Followers: 12) Indian Journal of Animal Nutrition       (Followers: 1) Indian Journal of Extension Education       (Followers: 2) Indian Journal of Horticulture       (Followers: 2) Indian Journal of Traditional Knowledge (IJTK)       (Followers: 1) Indonesian Journal of Agricultural Science       (Followers: 2) Information Processing in Agriculture Innovare Journal of Agricultural Science       (Followers: 1) Innovations in Agriculture Interciencia International Advances in Economic Research       (Followers: 6) International Dairy Journal       (Followers: 4) International Journal for Parasitology : Parasites and Wildlife       (Followers: 2) International Journal of Agricultural and Life Sciences International Journal of Agricultural Extension       (Followers: 1) International Journal of Agricultural Management and Development       (Followers: 2) International Journal of Agricultural Research       (Followers: 3) International Journal of Agricultural Research, Innovation and Technology       (Followers: 3) International Journal of Agricultural Resources, Governance and Ecology       (Followers: 2) International Journal of Agricultural Science and Food Technology International Journal of Agricultural Sustainability       (Followers: 12) International Journal of Agriculture and Forestry       (Followers: 6) International Journal of Agriculture Innovation, Technology and Globalisation International Journal of Agriculture, Environment and Food Sciences       (Followers: 1) International Journal of Agriculture, Forestry and Life Sciences International Journal of Agronomy       (Followers: 5) International Journal of Applied Agriculture and Apiculture Research       (Followers: 3) International Journal of Dairy Science       (Followers: 4) International Journal of Dairy Technology       (Followers: 2) International Journal of Food Science and Agriculture       (Followers: 4) International Journal of Fruit Science International Journal of Green Economics       (Followers: 6) International Journal of Hydrology Science and Technology       (Followers: 6) International Journal of Intercultural Relations       (Followers: 16) International Journal of Pest Management       (Followers: 2) International Journal of Postharvest Technology and Innovation       (Followers: 2) International Journal of Poultry Science       (Followers: 4) International Journal of Recycling of Organic Waste in Agriculture       (Followers: 1) International Journal of Secondary Metabolite International Journal of Social Economics       (Followers: 7) International Journal of Sustainable Agricultural Management and Informatics       (Followers: 1) International Journal of the Economics of Business       (Followers: 1) International Journal of Tropical Veterinary and Biomedical Research International Journal of Vegetable Science International Journal on Food, Agriculture and Natural Resources : IJ-FANRES International Letters of Natural Sciences International Multidisciplinary Research Journal International Review of Applied Economics       (Followers: 6) International Review of Economics       (Followers: 4) International Scientific Journal of Engineering and Technology (ISJET) Invertebrate Reproduction & Development       (Followers: 5) Iranian Journal of Applied Animal Science       (Followers: 1) Irrigation and Drainage       (Followers: 12) Irrigation Australia: The Official Journal of Irrigation Australia       (Followers: 1) Italian Journal of Agronomy       (Followers: 4) Journal (Australian Native Plants Society. Canberra Region) Journal de la Recherche Scientifique de l'Universite de Lome Journal für Kulturpflanzen Journal of Advances in Agriculture       (Followers: 5) Journal of Agrarian Change       (Followers: 3) Journal of Agricultural & Food Information       (Followers: 5) Journal of Agricultural and Environmental Ethics       (Followers: 8) Journal of Agricultural and Food Chemistry       (Followers: 9) Journal of Agricultural and Marine Sciences Journal of Agricultural Chemistry and Environment       (Followers: 3) Journal of Agricultural Economics       (Followers: 31) Journal of Agricultural Engineering       (Followers: 2) Journal of Agricultural Engineering       (Followers: 1) Journal of Agricultural Extension       (Followers: 1) Journal of Agricultural Extension and Rural Development       (Followers: 3) Journal of Agricultural Meteorology Journal of Agricultural Production Journal of Agricultural Research and Development Journal of Agricultural Research and Extension       (Followers: 1) Journal of Agricultural Science       (Followers: 9) Journal of Agricultural Science       (Followers: 4) Journal of Agricultural Sciences       (Followers: 1) Journal of Agricultural Sciences Journal of Agricultural Studies       (Followers: 1) Journal of Agricultural, Biological & Environmental Statistics       (Followers: 11) Journal of Agriculture Journal of Agriculture Journal of Agriculture and Ecology Research International Journal of Agriculture and Food Research Journal of Agriculture and Food Sciences       (Followers: 1) Journal of Agriculture and Natural Resources Journal of Agriculture and Natural Resources Sciences       (Followers: 3) Journal of Agriculture and Rural Development in the Tropics and Subtropics       (Followers: 4) Journal of Agriculture and Social Research (JASR)       (Followers: 5) Journal of Agriculture and Sustainability       (Followers: 6) Journal of Agriculture, Food Systems, and Community Development       (Followers: 3) Journal of Agriculture, Forestry and the Social Sciences       (Followers: 5) Journal Of Agrobiotechnology Journal of Agromedicine       (Followers: 1) Journal of Agronomy       (Followers: 7) Journal of Anatolian Environmental and Animal Sciences Journal of Animal Science and Products Journal of Animal Science, Biology and Bioeconomy Journal of Apicultural Science       (Followers: 2) Journal of Applied Communications Journal of Applied Ecology       (Followers: 207) Journal of Arid Land Journal of Asia-Pacific Entomology       (Followers: 6) Journal of Biological Control       (Followers: 2) Journal of Biology, Agriculture and Healthcare       (Followers: 2) Journal of Biosystems Engineering Journal of Buffalo Science Journal of Cereal Research       (Followers: 1) Journal of Cereal Science       (Followers: 3) Journal of Citrus Pathology Journal of Competition Law and Economics       (Followers: 34) Journal of Cotton Research Journal of Dairy Research       (Followers: 4) Journal of Dairy Science       (Followers: 12) Journal of Degraded and Mining Lands Management       (Followers: 1) Journal of Economic Surveys       (Followers: 23) Journal of Environmental and Agricultural Studies       (Followers: 1) Journal of Environmental Science and Health, Part B: Pesticides, Food Contaminants, and Agricultural Wastes       (Followers: 5) Journal of Essential Oil Research       (Followers: 3) Journal of Extension Education       (Followers: 1) Journal of Family and Economic Issues       (Followers: 4) Journal of Food Protection(R)       (Followers: 7) Journal of Food Security       (Followers: 2) Journal of Food Security and Agriculture       (Followers: 1) Journal of Halal Product and Research Journal of Horticultural Science and Biotechnology Journal of Horticultural Sciences       (Followers: 2) Journal of Horticulture and Postharvest Research Journal of Industrial Hemp Journal of Integrative Agriculture       (Followers: 4) Journal of Kerbala for Agricultural Sciences Journal of Land and Rural Studies       (Followers: 10) Journal of Modern Agriculture       (Followers: 4) Journal of Natural Resources and Development       (Followers: 2) Journal of Natural Sciences Research       (Followers: 1) Journal of Nepal Agricultural Research Council

First | 1 2 3 4

Similar Journals
 Journal of Agricultural, Biological & Environmental StatisticsJournal Prestige (SJR): 0.765 Citation Impact (citeScore): 1Number of Followers: 11      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1085-7117 - ISSN (Online) 1537-2693 Published by Springer-Verlag  [2469 journals]
• A Two-Species Occupancy Model with a Continuous-Time Detection Process
Reveals Spatial and Temporal Interactions

Abstract: Abstract Detection/non-detection data are increasingly collected in continuous time, e.g., via camera traps or acoustic sensors. Application of occupancy modeling approaches to these datasets typically requires discretizing the dataset to detections over individual days or weeks, which precludes analysis of temporal interactions between species or covariate relationships that change over fine temporal scales. To address this limitation, we developed a two-species occupancy model that assumes a temporal point process detection model. This model permits simultaneous analysis of species interactions in space (i.e., site occupancy) and time (i.e., activity patterns). The model is also capable of estimating the amount of time animals are available for detection, i.e., availability. We applied the model to detections of white-tailed deer (Odocoileus virginianus) and coyote (Canis latrans) collected via camera trapping. We found evidence of both temporal and spatial interactions between deer and coyote. Detection intensity of deer was greater and proportionally more diurnal where coyotes were present. At hunted sites, coyotes were more likely to occur at sites where deer were also present (and vice versa). These results highlight how two-species occupancy models with a continuous-time detection process can be used to infer temporal interactions between species. Our approach broadens the set of questions ecologists can ask regarding both spatial and temporal interactions between species, as well as fine-scale temporal covariates (e.g., weather). Our model should be increasingly applicable given the increasing availability of ecological data collected in continuous time. Supplementary materials accompanying this paper appear on-line
PubDate: 2022-06-01

• Probabilistic Forecasts of Arctic Sea Ice Thickness

Abstract: Abstract In recent decades, warming temperatures have caused sharp reductions in the volume of sea ice in the Arctic Ocean. Predicting changes in Arctic sea ice thickness is vital in a changing Arctic for making decisions about shipping and resource management in the region. We propose a statistical spatio-temporal two-stage model for sea ice thickness and use it to generate probabilistic forecasts up to three months into the future. Our approach combines a contour model to predict the ice-covered region with a Gaussian random field to model ice thickness conditional on the ice-covered region. Using the most complete estimates of sea ice thickness currently available, we apply our method to forecast Arctic sea ice thickness. Point predictions and prediction intervals from our model offer comparable accuracy and improved calibration compared with existing forecasts. We show that existing forecasts produced by ensembles of deterministic dynamic models can have large errors and poor calibration. We also show that our statistical model can generate good forecasts of aggregate quantities such as overall and regional sea ice volume. Supplementary materials accompanying this paper appear on-line.
PubDate: 2022-06-01

• Sample Design and Estimation When Using a Web-Scraped List Frame and
Capture-Recapture Methods

Abstract: Abstract Surveys are often based on a sample drawn from a list frame. In recent years, the percentage of target population units on the list frames has been decreasing, making it important to adjust for this undercoverage in the estimation process. Multiple-frame methods generally assume that the union of the available list frames is equal to the target population; however, this assumption is often not satisfied, especially for hard-to-survey populations. The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service has explored the use of web-scraped list frames to assess undercoverage of the NASS list frame, which is comprised of all known farms and potential farms in the USA. In 2020, NASS conducted the National Farmers Market Mangers Survey. Because NASS does not include farmers markets on its list frame, the USDA Agricultural Marketing Service (AMS) business register of farmers markets was the only list frame initially available. To assess its undercoverage, a web-scraped list frame was developed, and capture-recapture methods provided the foundation for estimation. This study made two advances in the use of capture-recapture methods when conducting a survey with two list frames. First, because record linkage was conducted prior to drawing the samples, the sample design incorporated information identifying records on only the AMS business register, on only the web-scraped list frame, or on both frames. Second, a composite estimator for this overlap design allowed full use of all sample information to produce survey estimates. Directions for future research are highlighted. Supplementary materials accompanying this paper appear online.
PubDate: 2022-06-01

• Hidden Markov and Semi-Markov Models When and Why are These Models Useful
for Classifying States in Time Series Data'

Abstract: Abstract Hidden Markov models (HMMs) and their extensions have proven to be powerful tools for classification of observations that stem from systems with temporal dependence as they take into account that observations close in time are likely generated from the same state (i.e., class). When information on the classes of the observations is available in advanced, supervised methods can be applied. In this paper, we provide details for the implementation of four models for classification in a supervised learning context: HMMs, hidden semi-Markov models (HSMMs), autoregressive-HMMs, and autoregressive-HSMMs. Using simulations, we study the classification performance under various degrees of model misspecification to characterize when it would be important to extend a basic HMM to an HSMM. As an application of these techniques we use the models to classify accelerometer data from Merino sheep to distinguish between four different behaviors of interest. In particular in the field of movement ecology, collection of fine-scale animal movement data over time to identify behavioral states has become ubiquitous, necessitating models that can account for the dependence structure in the data. We demonstrate that when the aim is to conduct classification, various degrees of model misspecification of the proposed model may not impede good classification performance unless there is high overlap between the state-dependent distributions, that is, unless the observation distributions of the different states are difficult to differentiate. Supplementary materials accompanying this paper appear on-line.
PubDate: 2022-06-01

• An Extreme Value Bayesian Lasso for the Conditional Left and Right Tails

Abstract: Abstract We introduce a novel regression model for the conditional left and right tail of a possibly heavy-tailed response. The proposed model can be used to learn the effect of covariates on an extreme value setting via a Lasso-type specification based on a Lagrangian restriction. Our model can be used to track if some covariates are significant for the lower values, but not for the (right) tail—and vice versa; in addition to this, the proposed model bypasses the need for conditional threshold selection in an extreme value theory framework. We assess the finite-sample performance of the proposed methods through a simulation study that reveals that our method recovers the true conditional distribution over a variety of simulation scenarios, along with being accurate on variable selection. Rainfall data are used to showcase how the proposed method can learn to distinguish between key drivers of moderate rainfall, against those of extreme rainfall. Supplementary materials accompanying this paper appear online.
PubDate: 2022-06-01

• Bayesian Analysis of Nonnegative Data Using Dependency-Extended Two-Part
Models

PubDate: 2022-06-01

• Population Size Estimation Using Zero-Truncated Poisson Regression with
Measurement Error

Abstract: Abstract Population size estimation is an important research field in biological sciences. In practice, covariates are often measured upon capture on individuals sampled from the population. However, some biological measurements, such as body weight, may vary over time within a subject’s capture history. This can be treated as a population size estimation problem in the presence of covariate measurement error. We show that if the unobserved true covariate and measurement error are both normally distributed, then a naïve estimator without taking into account measurement error will under-estimate the population size. We then develop new methods to correct for the effect of measurement errors. In particular, we present a conditional score and a nonparametric corrected score approach that are both consistent for population size estimation. Importantly, the proposed approaches do not require the distribution assumption on the true covariates; furthermore, the latter does not require normality assumptions on the measurement errors. This is highly relevant in biological applications, as the distribution of covariates is often non-normal or unknown. We investigate finite sample performance of the new estimators via extensive simulated studies. The methods are applied to real data from a capture–recapture study. Supplementary materials accompanying this paper appear on-line.
PubDate: 2022-06-01

• Improving Wildlife Population Inference Using Aerial Imagery and Entity
Resolution

Abstract: Abstract Recent technological advancements have seen a rapid growth in the use of imagery data to estimate the abundance and spatial distribution of animal populations. However, the value of imagery data may not be fully exploited under traditional analytical frameworks. We developed a method that leverages aerial imagery data for population modeling through entity resolution, a technique that stochastically links the same individual across multiple images. Resolving duplicate individuals in overlapping images that are distorted requires realigning observed point patterns optimally; however, popular machine learning algorithms for image stitching do not often account for alignment uncertainty. Moreover, duplicated individuals can provide insight about detection probability when overlaps are viewed as replicate surveys. Our model resolves individual identities by linking observed locations to latent activity centers and estimates total population as informed by the linkage structure. We developed a hierarchical framework to achieve entity resolution and abundance estimation cohesively, thereby avoiding single-direction error propagation that is common in two-stage models. We illustrate our method through simulation and a case study using aerial images of sea otters in Glacier Bay, Alaska. Supplementary materials accompanying this paper appear on-line
PubDate: 2022-06-01

• Estimation of Multivariate Dependence Structures via Constrained Maximum
Likelihood

Abstract: Abstract Estimating high-dimensional dependence structures in models of multivariate datasets is an ongoing challenge. Copulas provide a powerful and intuitive way to model dependence structure in the joint distribution of disparate types of variables. Here, we propose an estimation method for Gaussian copula parameters based on the maximum likelihood estimate of a covariance matrix that includes shrinkage and where all of the diagonal elements are restricted to be equal to 1. We show that this estimation problem can be solved using a numerical solution that optimizes the problem in a block coordinate descent fashion. We illustrate the advantage of our proposed scheme in providing an efficient estimate of sparse Gaussian copula covariance parameters using a simulation study. The sparse estimate was obtained by regularizing the constrained problem using either the least absolute shrinkage and selection operator (LASSO) or the adaptive LASSO penalty, applied to either the covariance matrix or the inverse covariance (precision) matrix. Simulation results indicate that our method outperforms conventional estimates of sparse Gaussian copula covariance parameters. We demonstrate the proposed method for modelling dependence structures through an analysis of multivariate groundfish abundance data obtained from annual bottom trawl surveys in the northeast Pacific from 2014 to 2018. Supplementary materials accompanying this paper appear on-line.
PubDate: 2022-06-01

• A Spatial Modeling Framework for Monitoring Surveys with Different
Sampling Protocols with a Case Study for Bird Abundance in Mid-Scandinavia

Abstract: Abstract Quantifying the total number of individuals (abundance) of species is the basis for spatial ecology and biodiversity conservation. Abundance data are mostly collected through professional surveys as part of monitoring programs, often at a national level. These surveys rarely follow exactly the same sampling protocol in different countries, which represents a challenge for producing biogeographical abundance maps based on the transboundary information available covering more than one country. Moreover, not all species are properly covered by a single monitoring scheme, and countries typically collect abundance data for target species through different monitoring schemes. We present a new methodology to model total abundance by merging count data information from surveys with different sampling protocols. The proposed methods are used for data from national breeding bird monitoring programs in Norway and Sweden. Each census collects abundance data following two different sampling protocols in each country, i.e., these protocols provide data from four different sampling processes. The modeling framework assumes a common Gaussian Random Field shared by both the observed and true abundance with either a linear or a relaxed linear association between them. The models account for particularities of each sampling protocol by including terms that affect each observation process, i.e., accounting for differences in observation units and detectability. Bayesian inference is performed using the Integrated Nested Laplace Approximation (INLA) and the Stochastic Partial Differential Equation (SPDE) approach for spatial modeling. We also present the results of a simulation study based on the empirical census data from mid-Scandinavia to assess the performance of the models under model misspecification. Finally, maps of the expected abundance of birds in our study region in mid-Scandinavia are presented with uncertainty estimates. We found that the framework allows for consistent integration of data from surveys with different sampling protocols. Further, the simulation study showed that models with a relaxed linear specification are less sensitive to misspecification, compared to the model that assumes linear association between counts. Relaxed linear specifications of total bird abundance in mid-Scandinavia improved both goodness of fit and the predictive performance of the models.
PubDate: 2022-05-22

• A Case-Crossover Study of the Impact of the Modifying Industrial
Operations Protocol on the Frequency of Industrial Forestry-Caused

Abstract: Abstract Wildland fire prevention and mitigation is of mutual interest to both government and the forest industry. In 1989, the Ontario Ministry of Natural Resources and Forestry introduced the Woods Modification Guidelines that provided rules on how forestry operations should be modified based on local fire danger conditions. Those guidelines were replaced by the Modifying Industrial Operations Protocol (MIOP) in 2008. One objective of MIOP is to allow forestry operations to be done safely for as long as possible as the fire danger increases. We investigate the impacts of these sets of regulations on the frequency of industrial forestry-caused (IDF) wildland fires in the province of Ontario, Canada. Data from 1976 to 2019 are analyzed. A case-crossover study finds no evidence to suggest that MIOP’s greater flexibility in operating hours has increased the probability of IDF fire occurrences. This result indicates that MIOP’s regulations have had the desired effect of allowing longer working hours on days with heightened fire risk without adding to the seasonal wildland fire load.
PubDate: 2022-04-29

• Incorporating Historical Data When Determining Sample Size Requirements
for Aquatic Toxicity Experiments

Abstract: Abstract In aquatic toxicity tests, responses of interest from organisms exposed to varying concentration levels of the toxicant or other adverse treatment are recorded. These responses are modeled as functions of the concentration and the concentration associated with specified levels of estimated adverse effect are used in risk management. While aquatic toxicity analyses often focus on outcomes from a single experiment, laboratories commonly have a history of conducting such experiments using the same species, following a similar experimental protocol. So it is often reasonable to believe that the same underlying biological process generates the historical and current experiments. This connection may facilitate the design of more efficient experiments. In the present study, we propose a simulation-based Bayesian sample size determination approach using historical control outcomes as prior input and illustrate it using a C. dubia reproduction experiment with count outcomes. Simulation results show that precision of the potency estimates is improved via incorporation of historical data. For a standard EPA required test of 60 total organisms, when a single historical control study is incorporated assuming moderate relevance, the mean length (AL) of the $$95\%$$ interval of $$\mathrm{RI}_{25}$$ (the concentration associated with $$25\%$$ inhibition relative to control) is reduced by $$17\%$$ . So more precision is possible from the historical control data or a reduction of $$40\%$$ of the 60 organism would result in the same precision for a pre-specified AL criterion. The incorporation of multiple historical controls assuming moderate relevance would reduce AL by $$37\%$$ , translating into a reduction of $$70\%$$ of the current default sample size. Supplementary materials accompanying this paper appear online.
PubDate: 2022-04-06

• A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes
with an Application to Animal Behavior

Abstract: Abstract In animal behavior studies, a common goal is to investigate the causal pathways between an exposure and outcome, and a mediator that lies in between. Causal mediation analysis provides a principled approach for such studies. Although many applications involve longitudinal data, the existing causal mediation models are not directly applicable to settings where the mediators are measured on irregular time grids. In this paper, we propose a causal mediation model that accommodates longitudinal mediators on arbitrary time grids and survival outcomes simultaneously. We take a functional data analysis perspective and view longitudinal mediators as realizations of underlying smooth stochastic processes. We define causal estimands of direct and indirect effects accordingly and provide corresponding identification assumptions. We employ a functional principal component analysis approach to estimate the mediator process and propose a Cox hazard model for the survival outcome that flexibly adjusts the mediator process. We then derive a g-computation formula to express the causal estimands using the model coefficients. The proposed method is applied to a longitudinal data set from the Amboseli Baboon Research Project to investigate the causal relationships between early adversity, adult physiological stress responses, and survival among wild female baboons. We find that adversity experienced in early life has a significant direct effect on females’ life expectancy and survival probability, but find little evidence that these effects were mediated by markers of the stress response in adulthood. We further developed a sensitivity analysis method to assess the impact of potential violation to the key assumption of sequential ignorability. Supplementary materials accompanying this paper appear on-line.
PubDate: 2022-04-05

• Time-Varying Functional Principal Components for Non-Stationary EpCO
$$_2$$ 2 in Freshwater Systems

Abstract: Abstract Outgassing of carbon dioxide (CO $$_2$$ ) from river surface waters, estimated using partial pressure of dissolved CO $$_2$$ , has recently been considered an important component of the global carbon budget. However, little is still known about the high-frequency dynamics of CO $$_2$$ emissions in small-order rivers and streams. To analyse such highly dynamic systems, we propose a time-varying functional principal components analysis (FPCA) for non-stationary functional time series (FTS). This time-varying FPCA is performed in the frequency domain to investigate how the variability and auto-covariance structures in a FTS change over time. This methodology, and the associated proposed inference, enables investigation of the changes over time in the variability structure of the diurnal profiles of the partial pressure of CO $$_2$$ and identification of the drivers of those changes. By means of a simulation study, the performance of the time-varying dynamic FPCs is investigated under different scenarios of complete and incomplete FTS. Although the time-varying dynamic FPCA has been applied here to study the daily processes of consuming and producing CO $$_2$$ in a small catchment of the river Dee in Scotland, this methodology can be applied more generally to any dynamic time series.Supplementary materials accompanying this paper appear online.
PubDate: 2022-03-19

• Robust Functional Principal Component Analysis Based on a New Regression
Framework

Abstract: Abstract It is of great interest to conduct robust functional principal component analysis (FPCA) that can identify the major modes of variation in the stochastic process with the presence of outliers. A new robust FPCA method is proposed in a new regression framework. An M-estimator for the functional principal components is developed based on the Huber’s loss by iteratively fitting the residuals from the Karhunen–Lovève expansion for the stochastic process under the robust regression framework. Our method can naturally accommodate sparse and irregularly-sampled data. When the functional data have outliers, our method is shown to render stable and robust estimates of the functional principal components; when the functional data have no outliers, we show via simulation studies that the performance of our approach is similar to that of the conventional FPCA method. The proposed robust FPCA method is demonstrated by analyzing the Hawaii ocean oxygen data and the kidney glomerular filtration rates for patients after renal transplantation.
PubDate: 2022-03-17

• Spatial Modeling of Day-Within-Year Temperature Time Series: An
Examination of Daily Maximum Temperatures in Aragón, Spain

Abstract: Abstract Acknowledging a considerable literature on modeling daily temperature data, we propose a multi-level spatiotemporal model which introduces several innovations in order to explain the daily maximum temperature in the summer period over 60 years in a region containing Aragón, Spain. The model operates over continuous space but adopts two discrete temporal scales, year and day within year. It captures temporal dependence through autoregression on days within year and also on years. Spatial dependence is captured through spatial process modeling of intercepts, slope coefficients, variances, and autocorrelations. The model is expressed in a form which separates fixed effects from random effects and also separates space, years, and days for each type of effect. Motivated by exploratory data analysis, fixed effects to capture the influence of elevation, seasonality, and a linear trend are employed. Pure errors are introduced for years, for locations within years, and for locations at days within years. The performance of the model is checked using a leave-one-out cross-validation. Applications of the model are presented including prediction of the daily temperature series at unobserved or partially observed sites and inference to investigate climate change comparison. Supplementary materials accompanying this paper appear online.
PubDate: 2022-03-13

• Greater Than the Sum of its Parts: Computationally Flexible Bayesian
Hierarchical Modeling

Abstract: Abstract We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase efficiency by allowing computations to take place in parallel form using software that is most appropriate for each data partition. The full hierarchical model is then approximated by the product of independent normal distributions for the data component of the model. In the second stage, the Bayesian maximum a posteriori (MAP) estimator is found by maximizing the approximated posterior density with respect to the parameters. If the parameters of the model can be represented as normally distributed random effects, then the second-stage optimization is equivalent to fitting a multivariate normal linear mixed model. We consider a third stage that updates the estimates of distinct parameters for each data partition based on the results of the second stage. The method is demonstrated with two ecological data sets and models, a generalized linear mixed effects model (GLMM) and an integrated population model (IPM). The multistage results were compared to estimates from models fit in single stages to the entire data set. In both cases, multistage results were very similar to a full MCMC analysis. Supplementary materials accompanying this paper appear online.
PubDate: 2022-03-09
DOI: 10.1007/s13253-021-00485-9

• Interpolation of Precipitation Extremes on a Large Domain Toward IDF Curve
Construction at Unmonitored Locations

Abstract: Abstract An intensity–duration–frequency (IDF) curve describes the relationship between rainfall intensity and duration for a given return period and location. Such curves are obtained through frequency analysis of rainfall data and commonly used in infrastructure design, flood protection, water management, and urban drainage systems. However, they are typically available only in sparse locations. Data for other sites must be interpolated as the need arises. This paper describes how extreme precipitation of several durations can be interpolated to compute IDF curves on a large, sparse domain. In the absence of local data, a reconstruction of the historical meteorology is used as a covariate for interpolating extreme precipitation characteristics. This covariate is included in a hierarchical Bayesian spatial model for extreme precipitations. This model is especially well suited for a covariate gridded structure, thereby enabling fast and precise computations. As an illustration, the methodology is used to construct IDF curves over Eastern Canada. An extensive cross-validation study shows that at locations where data are available, the proposed method generally improves on the current practice of Environment and Climate Change Canada which relies on a moment-based fit of the Gumbel extreme-value distribution.
PubDate: 2022-03-04
DOI: 10.1007/s13253-022-00491-5

• Review of “Using R for Modelling and Quantitative Methods in

PubDate: 2022-03-02
DOI: 10.1007/s13253-022-00492-4

• Measuring abundance: Methods for the Estimation of Population Size and
Species Richness

PubDate: 2022-03-01
DOI: 10.1007/s13253-021-00474-y

JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762