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A case-based method of selecting covariates for digital soil mapping

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Journal of Integrative Agriculture 2020年第8期19卷 2127-2136页

作者： LIANG Peng QIN Cheng-zhi ZHU A-xing HOU Zhi-wei FAN Nai-qing WANG Yi-jie State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101P.R.China University of Chinese Academy of Sciences Beijing 100049P.R.China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application School of GeographyNanjing Normal UniversityNanjing 210097P.R.China Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023P.R.China Department of Geography University of Wisconsin-MadisonMadisonWI 53706USA

Selecting a proper set of covariates is one of the most important factors that influence the accuracy of digital soil mapping(DSM).The statistical or machine learning methods for selecting DSM covariates are not available for those situations with limited *** solve the problem,this paper proposed a case-based method which could formalize the covariate selection knowledge contained in practical DSM *** proposed method trained Random Forest(RF)classifiers with DSM cases extracted from the practical DSM applications and then used the trained classifiers to determine whether each one potential covariate should be used in a new DSM *** this study,we took topographic covariates as examples of covariates and extracted 191 DSM cases from 56 peer-reviewed journal articles to evaluate the performance of the proposed case-based method by Leave-One-Out cross *** with a novices’commonly-used way of selecting DSM covariates,the proposed case-based method improved more than 30%accuracy according to three quantitative evaluation indices(i.e.,recall,precision,and F1-score).The proposed method could be also applied to selecting the proper set of covariates for other similar geographical modeling domains,such as landslide susceptibility mapping,and species distribution modeling.

关键词： digital soil mapping covariates case-based reasoning Random Forest

Weighted Least Squares Method for the Accelerated Failure Time Model with Auxiliary covariates

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Acta Mathematica Sinica,English Series 2019年第7期35卷 1163-1178页

作者： Ling Hui JIN Yan Yan LIU Lang WU School of Mathematics and Statistics Wuhan University Wuhan 430072 P. R. China City College Wuhan University of Science and Technology Wuhan 430083 P. R. China Department of Statistics University of British Columbia Vancouver Canada

This paper deals with the analysis of accelerated failure time model when the primary covariate is subject to missing. We assume that the true covariate is measured precisely on a randomly chosen validation set, whereas auxiliary information for primary covariate is available to all study subjects. The asymptotic properties for the proposed estimator are developed and the simulation studies show that the efficiency gain is remarkable compared to the method using only the validation sample. A real example is also provided as an illustration.

关键词： Survival analysis accelerated failure time model auxiliary covariates validation sample weighted least squares estimation

covariates of Disability-Profile Transitions in Older People Living at Home

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Journal of Biosciences and Medicines 2014年第3期2卷 25-36页

作者： Michel Raiche Rejean Hebert Marie-France Dubois N’Deye Rokhaya Gueye Nicole Dubuc Research Centre on Aging Sherbrooke University Geriatrics InstituteSherbrookeCanada Faculty of Medicine and Health Sciences Universitede SherbrookeSherbrookeCanada Faculty of Arts&Faculty of sciences Universitede Saint-BonifaceWinnipegCanada

The objective of this study was to explore the relationship of sociodemographic, clinical, and health-services use-related variables with transitions between disability-based profiles. In a longitudinal study of 1386 people aged 75 and over living in the community at baseline, disabilities were assessed annually for up to four years with the Functional Autonomy Measurement System (SMAF), which generates 14 Iso-SMAF profiles. These profiles are grouped into 4 disability states, which are predominant alterations in instrumental activities of daily living (IADLs), mobility, mental functions as well as severe and mixed disabilities. Continuous-time, multi-state Markov modeling was used to identify the factors associated with transitions made by older people between these states and to institutionalization and death. Greater age and receiving help for ADL were associated with four transitions, while altered cognitive functions and hospitalization were associated with three, all involving more decline or less recovery. From mild IADL profiles, men have a higher risk of transitioning to intermediate predominantly mental profiles, while women are at higher risk of transitioning to intermediate predominantly mobility profiles. Unmet needs are associated with deterioration, from mild IADL to intermediate predominantly mobility profiles. These results help understanding the complex progression of disabilities in older people.

关键词： Aged Disability Profiles Transitions covariates Multistate Model Case-Mix Classification Longitudinal

A weighted Wilcoxon estimate for the covariate-specific ROC curve

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Science China Mathematics 2017年第9期60卷 1705-1716页

作者： ZHANG QingZhao DUAN XiaoGang ZHOU XiaoHua School of Economics and Wang Yanan Institute for Studies in Economics Xiamen University Department of Statistics Beijing Normal University Department of Biostatistics University of Washington

The covariate-specific receiver operating characteristic(ROC) curve is an important tool for evaluating the classification accuracy of a diagnostic test when it is associated with certain covariates. In this paper,a weighted Wilcoxon estimator is constructed for estimating this curve under the framework of location-scale model for the test result. The asymptotic normality is established, both for the regression parameter estimator and the estimator for the covariate-specific ROC curve at a fixed false positive point. Simulation results show that the Wilcoxon estimator compares favorably to its main competitors in terms of the standard error, especially when outliers exist in the covariates. As an illustration, the new procedure is applied to the dementia data from the national Alzheimer's coordinating center.

关键词： covariates location-scale model receiver operating characteristic curve Wilcoxon method

Classification of Soil Types Using Geographic Object-Based Image Analysis and Random Forests

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Pedosphere 2018年第6期28卷 913-925页

作者： Andrei DORNIK Lucian DRAGUT Petru URDEA Department of Geography West University of Timioara

There is increasing interest in developing automatic procedures to segment landscapes into soil spatial entities that replace conventional, expensive manual procedures for delineating and classifying soils. Geographic object-based image analysis(GEOBIA)partitions remote sensing imagery or digital elevation models into homogeneous image objects based on image segmentation. We used an object-based methodology for the detailed delineation and classification of soil types using digital maps of topography and vegetation as soil covariates, based on the Random Forests(RF) classifier. We compared the object-based method's results with those of a pixel-based classification using the same classifier. We used 18 digital elevation model derivatives and 5 remote sensing indices that were related to vegetation cover and soil. Using 171 soil profiles with their associated environmental variable values,the RF method was used to identify the most important soil type predictors for use in the segmentation process. A stack of rastergeodatasets corresponding to the selected predictors was segmented using a multi-resolution segmentation algorithm, which resulted in homogeneous objects related to soil types. These objects were further classified as soil types using the same method, RF. We also conducted a pixel-based classification using the same classifier and soil profiles, and the resulting maps were assessed in terms of their accuracy using 30% of the soil profiles for validation. We found that GEOBIA was an effective method for soil type mapping, and was superior to the pixel-based approach. The optimized object-based soil map had an overall accuracy of 58%, which was 10% higher than that of the optimized pixel-based map.

关键词： GEOBIA landscape segmentation soil classification soil covariates soil mapping

A Modified Regression Estimator for Single Phase Sampling in the Presence of Observational Errors

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Open Journal of Statistics 2022年第2期12卷 175-187页

作者： Nujayma M. A. Salim Christopher O. Onyango Department of Mathematics and Actuarial Science Kenyatta University Nairobi Kenya

In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariates were used and a case where the observational errors were in both the survey variable and the covariates was considered. The inclusion of observational errors was due to the fact that data collected through surveys are often not free from errors that occur during observation. These errors can occur due to over-reporting, under-reporting, memory failure by the respondents or use of imprecise tools of data collection. The expression of mean squared error (MSE) based on the obtained estimator has been derived to the first degree of approximation. The results of a simulation study show that the derived modified regression mean estimator under observational errors is more efficient than the mean per unit estimator and some other existing estimators. The proposed estimator can therefore be used in estimating a finite population mean, while considering observational errors that may occur during a study.

关键词： Estimate Regression covariates Single Phase Sampling Observational Errors Mean Squared Error

Fertility Mediating Factors in Sub-Saharan Africa （SSA）： Macro-Level Cross Sectional Perspectives

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Sociology Study 2016年第3期6卷 164-175页

作者： Hadgu Bariagaber University of Botswana Botswana

The aim of this paper is to investigate the mediating factors of fertility levels and trends in Sub-Saharan African CSSA） countries. The major sources of data of the study are the 2001 and 2002 UNDP （United Nations Development Program）, covering 42 SSA countries. The selected antecedent variables of fertility levels and trends include： GDP per capita; female adult literacy rate; infant mortality rate; contraceptive prevalence rate; breastfeeding; access to health services; undernutrition; and level of urbanization. The main analytical techniques of the study include simple central tendency descriptive statistics and advanced multiple regression models. The outputs of the correlation matrix and regression models are shown to influence the levels and trends of fertility positively or negatively, increasing or decreasing fertility levels among the countries. But, contraceptive prevalence level appears to be relatively powerful at 5% significance level, relatively more decline with the level of urbanization and positively related with poverty level, which cumulatively gives rise to 5.9 total fertility rate per woman. Accordingly, with declining mortality and high fertility level, the era of rapid population growth in SSA countries has not yet been over with all its implications on sustainable development.

关键词： Fertility covariates correlation central tendency matrix multiple regression

Plausible combinations: An improved method to evaluate the covariate structure of Cormack-Jolly-Seber mark-recapture models

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Open Journal of Ecology 2013年第1期3卷 11-22页

作者： Jeffrey F. Bromaghin Trent L. McDonald Steven C. Amstrup Polar Bears International Bozeman USA US Geological Survey Alaska Science Center Anchorage USA Western Ecosystems Technology Inc. Cheyenne USA

Mark-recapture models are extensively used in quantitative population ecology, providing estimates of population vital rates, such as survival, that are difficult to obtain using other methods. Vital rates are commonly modeled as functions of explanatory covariates, adding considerable flexibility to mark-recapture models, but also increasing the subjectivity and complexity of the modeling process. Consequently, model selection and the evaluation of covariate structure remain critical aspects of mark-recapture modeling. The difficulties involved in model selection are compounded in Cormack-Jolly-Seber models because they are composed of separate sub-models for survival and recapture probabilities, which are conceptualized independently even though their parameters are not statistically independent. The construction of models as combinations of sub-models, together with multiple potential covariates, can lead to a large model set. Although desirable, estimation of the parameters of all models may not be feasible. Strategies to search a model space and base inference on a subset of all models exist and enjoy widespread use. However, even though the methods used to search a model space can be expected to influence parameter estimation, the assessment of covariate importance, and therefore the ecological interpretation of the modeling results, the performance of these strategies has received limited investigation. We present a new strategy for searching the space of a candidate set of Cormack-Jolly-Seber models and explore its performance relative to existing strategies using computer simulation. The new strategy provides an improved assessment of the importance of covariates and covariate combinations used to model survival and recapture probabilities, while requiring only a modest increase in the number of models on which inference is based in comparison to existing techniques.

关键词： Capture-Recapture Survival Model Building Model Selection Model Averaging Multi-Model Inference covariates Covariate Weights CJS Akaike’s Information Criterion AIC AICC

Temporal and Spatial Factors Affecting the Nature of Genotype x Environment Interaction in Sugarcane (<i>Saccharum officinarum L.</i>) under Ethiopian Agro-Climatic Conditions: An Integrated Approach

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American Journal of Plant Sciences 2017年第7期8卷 1721-1749页

作者： Mebrahtom Ftwi Firew Mekbib Eyasu Abraha Ethiopian Sugar Corporation Research and Development Center Nazareth Ethiopia Haramaya University Dire Dawa Ethiopia Tigray Agricultural Research Institute Mekelle Ethiopia

Analytical approaches are important for identification the causes of Genotype x environment interaction (GEI) in multi-environment trials (MET). The objectives of this investigation were to explore the nature and causes of the GEI in sugarcane under Ethiopian agro ecological conditions. Data of Cane yield, recoverable sucrose% and sugar yield obtained from 13 test environments were subjected to Additive Main and Multiplicative Interaction (AMMI2) for empirical study. For environmental characterization, mean values of twenty seven temporal and spatial factors were subjected to Principal Component Analysis (PCA). Moreover, to identify the environmental factors affecting the patterns of GEI and its components, mean values of these factors were correlated with environment AMMI2 IPCA scores of the yield traits studied. Results of the present study indicated that the GEI accounted for larger variation than the genotypic effects which suggested its importance and complexity. The PCA bi-plot successfully identified the environmental factors that most discriminated the test environments (crop years and locations). The correlation analysis between the environmental factors and environmental AMMI2 IPCA scores adequately identified the environmental factors affecting GEI and its components. Soil organic matter% and soil clay%, pan evaporation at establishment, relative humidity at growth stage and harvesting month were the major factors that substantially affect the GEI interaction patterns in cane yield while maximum relative humidity recorded during the growing season, all temperature regimes recorded during the entire growing season and at different crop stages were the major factors that affect GEI in recoverable sucrose%. Moreover, the pattern of GEI in sugar yield was significantly affected by harvest age, percent of clay in soil, altitude, relative humidity at harvest and pan evaporation at establishment. For efficient environmental selection and networks, genotype evalu

关键词： AMMI Bi-Plots Environmental covariates Genotypes MET PCA Bi-Plots Test Environments