Use of models in large-area forest surveys： comparing model-assisted, model-based and hybrid estimation

This paper focuses on the use of models for increasing the precision of estimators in large-area forest surveys. It is motivated by the increasing availability of remotely sensed data, which facilitates the development of models predicting the variables of interest in forest surveys. We present, review and compare three different estimation frameworks where models play a core role： model-assisted, model-based, and hybrid estimation. The first two are well known, whereas the third has only recently been introduced in forest surveys. Hybrid inference mixes design- based and model-based inference, since it relies on a probability sample of auxiliary data and a model predicting the target variable from the auxiliary data.We review studies on large-area forest surveys based on model-assisted, model- based, and hybrid estimation, and discuss advantages and disadvantages of the approaches. We conclude that no general recommendations can be made about whether model-assisted, model-based, or hybrid estimation should be preferred. The choice depends on the objective of the survey and the possibilities to acquire appropriate field and remotely sensed data. We also conclude that modelling approaches can only be successfully applied for estimating target variables such as growing stock volume or biomass, which are adequately related to commonly available remotely sensed data, and thus purely field based surveys remain important for several important forest parameters.

Model-Assisted Estimators with Auxiliary Functional Data

Few studies focus on the application of functional data to the field of design-based survey sampling.In this paper,the scalar-onunction regression model-assisted method is proposed to estimate the finite population means with auxiliary functional data information.The functional principal component method is used for the estimation of functional linear regression model.Our proposed functional linear regression model-assisted(FLR-assisted)estimator is asymptotically design-unbiased,consistent under mild conditions.Simulation experiments and real data analysis show that the FLR-assisted estimators are more efficient than the Horvitz-Thompson estimators under different sampling designs.

Estimating the Variance of the Proportion of Contaminated Soil by Petroleum Spills Using Two-Dimensional Systematic Sampling under Different Approaches

In leading petroleum-producing countries like Kuwait, Brazil, Iran, Iraq and Mexico oil spills frequently occur on land, causing serious damage to crop fields. Soil remediation requires constant monitoring of the polluted area. One common monitoring method involves two-dimensional systematic sampling, which can be used to estimate the proportion of the contaminated soil and study the oil spills’ geographic distribution. A well-known issue using this sampling design involves the analytical derivation of variance of the sample mean (proportion), which requires at least two independent samples. To address the problem, this research proposed a variance estimator based on regression and a corrected estimator using the autocorrelation Geary Index under the model-assisted approach. The construction of the estimators was assisted by geo-statistical models by simulating an auxiliary variable. Similar populations to those in real oil spills were recreated, and the accuracy of proposed estimators was evaluated by comparing their performance with other well-known estimators. The factors considered in this simulation study were: a) the model for simulating the populations (exponential and wave), b) the mean and the variance of the process, c) the level of autocorrelation among units. Given the statistical and computing burdens (bias, ratio between estimated and real variance, convergence and computer time), under the exponential model, the regression estimator showed the best performance;and for the wave model, the corrected version performed even better.

A protocol of field-based phenotyping procedure for no-till wheat root system architecture based on data-driven model-assist

Field-based phenotyping(FBP)of crop root systemarchitecture(RSA)provides away to quantify the root growth and distribution in fieldwith a smaller scale.Studies on a better understanding of the interrelations between field crop root physiological traits,root developmental phases and environmental changes are hindered due to deficiency of in situ root system architecture testing and quantitative methods for field crop.The present study aimed to propose a protocol for field-based wheat root system architecture with technical details of key operational procedures.Phenotyping of RSA traits from root spatial coordinate data acquisition and visualization software presented scaled illustrations of wheat RSA dynamics and root developmental phases which also revealed the root topological heterogeneities,eitherwithin a plant oramong individuals.Percentage of horizontal and vertical soil coverage by root showed that root foraging capability along soil depth was better than within the horizontal dimension.In brief,our data indicated that FBP ofwheat RSA could be achieved using the protocol of datadriven model-assisted phenotyping procedure.The proposed protocol was demonstrated useful for FBP of RSAs.It was proved effective to illustrate the topological structures of the wheat root system and to quantify RSAderived parameters,this could be a useful tool for characterizing and analyzing the structural distortion,heterogeneous distribution and the soil space exploration characteristics of wheat root.

Inference after covariate-adaptive randomisation:aspects of methodology and theory

Covariate-adaptive randomisation has a more than 45 years of history of applications in clinical trials,in order to balance treatment assignments across prognostic factors that may have influence on the outcomes of interest.However,almost no theory had been developed for covariate-adaptive randomisation until a paper on the theory of testing hypotheses published in 2010.In this article,we review aspects of methodology and theory developed in the last decade for statistical inference under covariate-adaptive randomisation.Wefocus on issues such as whether a conventional procedure valid under the assumption that treatments are assigned completely at random is still valid or conservative when the actual randomisation is covariateadaptive,how a valid inference procedure can be obtained by modifying a conventional method or directly constructed by stratifying the covariates used in randomisation,whether inference procedures have different properties when covariate-adaptive randomisation schemes have different degrees of balancing assignments,and how to further adjust covariates in the inference procedures to gain more efficiency.Recommendations are made during the review and further research problems are discussed.