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Addressing nonresponse bias in forest inventory change estimation using response homogeneity classifications

Forest Ecosystems 2023年第1期10卷 125-131页

作者： James A.Westfall Mark D.Nelson U.S.Forest Service Northern Research StationYorkPAUSA U.S.Forest Service Northern Research StationSt.PaulMNUSA

Estimating amounts of change in forest resources over time is a key function of most national forest inventories(NFI). As this information is used broadly for many management and policy purposes, it is imperative that accurate estimations are made from the survey sample. Robust sampling designs are often used to help ensure representation of the population, but often the full sample is unrealized due to hazardous conditions or possibly lack of land access permission. Potentially, bias may be imparted to the sample if the nonresponse is nonrandom with respect to forest characteristics, which becomes more difficult to assess for change estimation methods that require measurements of the same sample plots at two points in time, i.e., remeasurement. To examine potential nonresponse bias in change estimates, two synthetic populations were constructed: 1) a typical NFI population consisting of both forest and nonforest plots, and 2) a population that mimics a large catastrophic disturbance event within a forested population. Comparisons of estimates under various nonresponse scenarios were made using a standard implementation of post-stratified estimation as well as an alternative approach that groups plots having similar response probabilities(response homogeneity). When using the post-stratified estimators, the amount of change was overestimated for the NFI population and was underestimated for the disturbance population, whereas the response homogeneity approach produced nearly unbiased estimates under the assumption of equal response probability within groups. These outcomes suggest that formal strategies may be needed to obtain accurate change estimates in the presence of nonrandom nonresponse.

关键词： Disturbance post-stratification Land use conversion Sample bias

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Observed versus estimated actual trend of COVID-19 case numbers in Cameroon:A data-driven modelling

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Infectious Disease Modelling 2023年第1期8卷 228-239页

作者： Arsène Brunelle Sandie Mathurin Cyrille Tejiokem Cheikh Mbacké Faye Achta Hamadou Aristide Abah Abah Serge Sadeuh Mbah Paul Alain Tagnouokam-Ngoupo Richard Njouom Sara Eyangoh Ngu Karl Abanda Maryam Diarra Slimane Ben Miled Maurice Tchuente Jules Brice Tchatchueng-Mbougua African Population and Health Research Center West Africa Regional OfficeDakarSenegal Centre Pasteur du Cameroon membre du Réseau International des Instituts PasteurCameroon Direction de la lutte contre les Maladiesépidémiques et les pandémies Ministere de la santépubliqueCameroon Fondation pour la recherche l'ingénierie et l'innovation Cameroon Institut Pasteur de Dakar DakarSenegal Institut Pasteur de Tunis TunisTunisia IRD UMI 209 UMMISCO University of Yaounde IP.O.Box 337YaoundeCameroon

Controlling the COVID-19 outbreak remains a challenge for Cameroon,as it is for many other countries *** number of confirmed cases reported by health authorities in Cameroon is based on observational data,which is not nationally *** actual extent of the outbreak from the time when the first case was reported in the country to now remains *** study aimed to estimate and model the actual trend in the number of COVID-19 new infections in Cameroon from March 05,2020 to May 31,2021 based on an observed disaggregated *** used a large disaggregated dataset,and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05,2020 to May 31,***,seasonal autoregressive integrated moving average(SARIMA)modeling was used for forecasting *** on the prospective MRP modeling findings,a total of about 7450935(30%)of COVID-19 cases was estimated from March 05,2020 to May 31,2021 in ***,the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 *** forecasting indicated a succession of two waves of the outbreak in the next two years following May 31,*** no action is taken,there could be many waves of the outbreak in the *** avoid such situations which could be a threat to global health,public health Abbreviations:ACF,Autocorrelation Function;AIC,Akaike information criterion;COVID-19,Coronavirus Disease 2019;MAE,Mean Absolute Error;MAPE,Mean Absolute Percentage Error;MASE,Mean Absolute Scaled Error;ME,Mean Error;MPE,Mean Percentage Error;MRP,Multilevel Regression and post-stratification;PACF,Partial Autocorrelation Function;PLACARD,Platform for Collecting,Analyzing and Reporting Data;SARIMA,Seasonal Autoregressive integrated moving average;SARS-CoV-2,Severe Acute Respiratory Syndrome Coronavirus 2.

关键词： Cameroon COVID-19 Forecasting Observed post-stratification Underestimated

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Calibration of a Confidence Interval for a Classification Accuracy

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Open Journal of Forestry 2021年第1期11卷 14-36页

作者： Steen Magnussen Natural Resources Canada Canadian Forest Service Victoria British Columbia Canada

Coverage of nominal 95% confidence intervals of a proportion estimated from a sample obtained under a complex survey design, or a proportion estimated from a ratio of two random variables, can depart significantly from its target. Effective calibration methods exist for intervals for a proportion derived from a single binary study variable, but not for estimates of thematic classification accuracy. To promote a calibration of confidence intervals within the context of land-cover mapping, this study first illustrates a common problem of under and over-coverage with standard confidence intervals, and then proposes a simple and fast calibration that more often than not will improve coverage. The demonstration is with simulated sampling from a classified map with four classes, and a reference class known for every unit in a population of 160,000 units arranged in a square array. The simulations include four common probability sampling designs for accuracy assessment, and three sample sizes. Statistically significant over- and under-coverage was present in estimates of user’s (UA) and producer’s accuracy (PA) as well as in estimates of class area proportion. A calibration with Bayes intervals for UA and PA was most efficient with smaller sample sizes and two cluster sampling designs.

关键词： Overall Accuracy Producer’s Accuracy User’s Accuracy Area Proportions Semi-Systematic Sampling post-stratification Stratified Random Sampling One-Stage Cluster Sampling Two-Stage Cluster Sampling

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