How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

On the Impact of Bootstrap in Stratified Random Sampling

In general the accuracy of mean estimator can be improved by stratified random sampling. In this paper, we provide an idea different from empirical methods that the accuracy can be more improved through bootstrap resampling method under some conditions. The determination of sample size by bootstrap method is also discussed, and a simulation is made to verify the accuracy of the proposed method. The simulation results show that the sample size based on bootstrapping is smaller than that based on central limit theorem.

Analysis of Methodology for the Application of Stratified Random Sampling with Optimum Allocation： The Case Study of Forest Bioenergy

In this paper, analysis of methodology was realized for the application of stratified random sampling with optimum allocation in the case of a subject of research which concerns the rural population and presents high differentiations among the three strata in which this population could be classified. The rural population of Evros Prefecture （Greece） with criterion the mean altitude of settlements was classified in three strata, the mountainous, semi-mountainous and fiat population for the estimation of mean consumption of forest fuelwood for covering of heating and cooking needs in households of these three strata. The analysis of this methodology includes： （1） the determination of total size of sample for entire the rural population and its allocation to the various strata; （2） the investigation of effectiveness of stratification with the technique of analysis of variance （One-Way ANOVA）; （3） the conduct of sampling research with the realization of face-to-face interviews in selected households and （4） the control of forms of the questionnaire and the analysis of data by using the statistical package for social sciences, SPSS for Windows. All data for the analysis of this methodology and its practical application were taken by the pilot sampling which was realized in each stratum. Relative paper was not found by the review of literature.

Characteristics and demographic factors of traditional Chinese medicine constitution types among elderly individuals in China:A national multistage cluster random study Author links open overlay panel

Objective To reveal the distribution characteristics and demographic factors of traditional Chinese medicine(TCM)constitution among elderly individuals in China.Methods Elderly individuals from seven regions in China were selected as samples in this study using a multistage cluster random sampling method.The basic information questionnaire and Constitution in Chinese Medicine Questionnaire(Elderly Edition)were used.Descriptive statistical analysis,chi-squared tests,and binary logistic regression analysis were used.Results The single balanced constitution(BC)accounted for 23.9%.The results of the major TCM constitution types showed that BC(43.2%)accounted for the largest proportion and unbalanced constitutions ranged from 0.9%to 15.7%.East China region(odds ratio[OR]=2.097;95%confidence interval[CI],1.912 to 2.301),married status(OR=1.341;95%CI,1.235 to 1.457),and managers(OR=1.254;95%CI,1.044 to 1.505)were significantly associated with BC.Age>70 years was associated with qi-deficiency constitution and blood stasis constitution(BSC).Female sex was significantly associated with yang-deficiency constitution(OR=1.646;95%CI,1.52 to 1.782).Southwest region was significantly associated with phlegm-dampness constitution(OR=1.809;95%CI,1.569 to 2.086).North China region was significantly associated with inherited special constitution(OR=2.521;95%CI,1.569 to 4.05).South China region(OR=2.741;95%CI,1.997 to 1.3.763),Central China region(OR=8.889;95%CI,6.676 to 11.835),senior middle school education(OR=2.442;95%CI,1.932 to 3.088),and managers(OR=1.804;95%CI,1.21 to 2.69)were significantly associated with BSC.Conclusions This study defined the distribution characteristics and demographic factors of TCM constitution in the elderly population.Adjusting and improving unbalanced constitutions,which are correlated with diseases,can help promote healthy aging through the scientific management of these demographic factors.

Local Polynomial Regression Estimator of the Finite Population Total under Stratified Random Sampling: A Model-Based Approach

In this paper, auxiliary information is used to determine an estimator of finite population total using nonparametric regression under stratified random sampling. To achieve this, a model-based approach is adopted by making use of the local polynomial regression estimation to predict the nonsampled values of the survey variable y. The performance of the proposed estimator is investigated against some design-based and model-based regression estimators. The simulation experiments show that the resulting estimator exhibits good properties. Generally, good confidence intervals are seen for the nonparametric regression estimators, and use of the proposed estimator leads to relatively smaller values of RE compared to other estimators.

Crowdsourced Sampling of a Composite Random Variable: Analysis, Simulation, and Experimental Test

A composite random variable is a product (or sum of products) of statistically distributed quantities. Such a variable can represent the solution to a multi-factor quantitative problem submitted to a large, diverse, independent, anonymous group of non-expert respondents (the “crowd”). The objective of this research is to examine the statistical distribution of solutions from a large crowd to a quantitative problem involving image analysis and object counting. Theoretical analysis by the author, covering a range of conditions and types of factor variables, predicts that composite random variables are distributed log-normally to an excellent approximation. If the factors in a problem are themselves distributed log-normally, then their product is rigorously log-normal. A crowdsourcing experiment devised by the author and implemented with the assistance of a BBC (British Broadcasting Corporation) television show, yielded a sample of approximately 2000 responses consistent with a log-normal distribution. The sample mean was within ~12% of the true count. However, a Monte Carlo simulation (MCS) of the experiment, employing either normal or log-normal random variables as factors to model the processes by which a crowd of 1 million might arrive at their estimates, resulted in a visually perfect log-normal distribution with a mean response within ~5% of the true count. The results of this research suggest that a well-modeled MCS, by simulating a sample of responses from a large, rational, and incentivized crowd, can provide a more accurate solution to a quantitative problem than might be attainable by direct sampling of a smaller crowd or an uninformed crowd, irrespective of size, that guesses randomly.

Quality Assessment of Attribute Data in GIS Based on Simple Random Sampling

On the basis of the principles of simple random sampling, the statistical model of rate of disfigurement (RD) is put forward and described in detail. According to the definition of simple random sampling for the attribute data in GIS, the mean and variance of the RD are deduced as the characteristic value of the statistical model in order to explain the feasibility of the accuracy measurement of the attribute data in GIS by using the RD. Moreover, on the basis of the mean and variance of the RD, the quality assessment method for attribute data of vector maps during the data collecting is discussed. The RD spread graph is also drawn to see whether the quality of the attribute data is under control. The RD model can synthetically judge the quality of attribute data, which is different from other measurement coefficients that only discuss accuracy of classification.

Second-order random wave solutions for interfacial internal waves in N-layer density-stratified fluid

This paper studies the random internal wave equations describing the density interface displacements and the velocity potentials of N-layer stratified fluid contained between two rigid walls at the top and bottom. The density interface displacements and the velocity potentials were solved to the second-order by an expansion approach used by Longuet-Higgins （1963） and Dean （1979） in the study of random surface waves and by Song （2004） in the study of second- order random wave solutions for internal waves in a two-layer fluid. The obtained results indicate that the first-order solutions are a linear superposition of many wave components with different amplitudes, wave numbers and frequencies, and that the amplitudes of first-order wave components with the same wave numbers and frequencies between the adjacent density interfaces are modulated by each other. They also show that the second-order solutions consist of two parts： the first one is the first-order solutions, and the second one is the solutions of the second-order asymptotic equations, which describe the second-order nonlinear modification and the second-order wave-wave interactions not only among the wave components on same density interfaces but also among the wave components between the adjacent density interfaces. Both the first-order and second-order solutions depend on the density and depth of each layer. It is also deduced that the results of the present work include those derived by Song （2004） for second-order random wave solutions for internal waves in a two-layer fluid as a particular case.

L-Moments Based Calibrated Variance Estimators Using Double Stratified Sampling

Variance is one of the most vital measures of dispersion widely employed in practical aspects.A commonly used approach for variance estimation is the traditional method of moments that is strongly influenced by the presence of extreme values,and thus its results cannot be relied on.Finding momentum from Koyuncu’s recent work,the present paper focuses first on proposing two classes of variance estimators based on linear moments(L-moments),and then employing them with auxiliary data under double stratified sampling to introduce a new class of calibration variance estimators using important properties of L-moments(L-location,L-cv,L-variance).Three populations are taken into account to assess the efficiency of the new estimators.The first and second populations are concerned with artificial data,and the third populations is concerned with real data.The percentage relative efficiency of the proposed estimators over existing ones is evaluated.In the presence of extreme values,our findings depict the superiority and high efficiency of the proposed classes over traditional classes.Hence,when auxiliary data is available along with extreme values,the proposed classes of estimators may be implemented in an extensive variety of sampling surveys.

A Note on the Precision of Stratified Systematic Sampling

Conflicting views had greeted the use of systematic sampling for sample selection and estimation in stratified sampling in terms of the precision of the population mean base on the inherent characteristics of the population. These conflicting views were analyzed using Cochran data (1977, p. 211) [1]. When the population units are ordered, variance of systematic sampling for all possible systematic samples provides equal, non-negative and most precise estimates for all the variance functions considered i.e. , unlike when a single systematic sample is used and when variance of simple random sampling is used to estimate selected systematic samples.

Random Route and Quota Sampling: Do They Offer Any Advantage over Probably Sampling Methods?

The aim of this paper is to compare sample quality across two probability samples and one that uses probabilistic cluster sampling combined with random route and quota sampling within the selected clusters in order to define the ultimate survey units. All of them use the face-to-face interview as the survey procedure. The hypothesis to be tested is that it is possible to achieve the same degree of representativeness using a combination of random route sampling and quota sampling (with substitution) as it can be achieved by means of household sampling (without substitution) based on the municipal register of inhabitants. We have found such marked differences in the age and gender distribution of the probability sampling, where the deviations exceed 6%. A different picture emerges when it comes to comparing the employment variables, where the quota sampling overestimates the economic activity rate (2.5%) and the unemployment rate (8%) and underestimates the employment rate (3.46%).

Likelihood Methods for Basic Stratified Sampling, with Application to Von Bertalanffy Growth Model Estimation

This paper mainly addresses maximum likelihood estimation for a response-selective stratified sampling scheme, the basic stratified sampling (BSS), in which the maximum subsample size in each stratum is fixed. We derived the complete-data likelihood for BSS, and extended it as a full-data likelihood by incorporating incomplete data. We also similarly extended the empirical proportion likelihood approach for consistent and efficient estimation. We conducted a simulation study to compare these two new approaches with the existing estimation methods in BSS. Our result indicates that they perform as well as the standard full information likelihood approach. Methods were illustrated using a growth model for fish size at age, including between-individual variability. One of our major conclusions is that the fully observed BSS data, the partially observed data used for stratification, and the sampling strategy are all important in constructing a consistent and efficient estimator.

Compromise Allocation for Combined Ratio Estimates of Population Means of a Multivariate Stratified Population Using Double Sampling in Presence of Non-Response

This paper is an attempt to work out a compromise allocation to construct combined ratio estimates under multivariate double sampling design in presence of non-response when the population mean of the auxiliary variable is unknown. The problem has been formulated as a multi-objective integer non-linear programming problem. Two solution procedures are developed using goal programming and fuzzy programming techniques. A numerical example is also worked out to illustrate the computational details. A comparison of the two methods is also carried out.

A New Estimator Using Auxiliary Information in Stratified Adaptive Cluster Sampling

In this paper, we study the estimators of the population mean in stratified adaptive cluster sampling by using the information of the auxiliary variable. Simulations showed that if the variable of interest (y) and the auxiliary variables (x,z) have high positive correlation then the estimate of the mean square error of the ratio estimators is less than the estimate of the mean square error of the product estimator. The estimators which use only one auxiliary variable were better than the estimators which use two auxiliary variables.

Assessment of Random Recruitment Assumption in Respondent-Driven Sampling in Egocentric Network Data

One of the key assumptions in respondent-driven sampling (RDS) analysis, called “random selection assumption,” is that respondents randomly recruit their peers from their personal networks. The objective of this study was to verify this assumption in the empirical data of egocentric networks. Methods: We conducted an egocentric network study among young drug users in China, in which RDS was used to recruit this hard-to-reach population. If the random recruitment assumption holds, the RDS-estimated population proportions should be similar to the actual population proportions. Following this logic, we first calculated the population proportions of five visible variables (gender, age, education, marital status, and drug use mode) among the total drug-use alters from which the RDS sample was drawn, and then estimated the RDS-adjusted population proportions and their 95% confidence intervals in the RDS sample. Theoretically, if the random recruitment assumption holds, the 95% confidence intervals estimated in the RDS sample should include the population proportions calculated in the total drug-use alters. Results: The evaluation of the RDS sample indicated its success in reaching the convergence of RDS compositions and including a broad cross-section of the hidden population. Findings demonstrate that the random selection assumption holds for three group traits, but not for two others. Specifically, egos randomly recruited subjects in different age groups, marital status, or drug use modes from their network alters, but not in gender and education levels. Conclusions: This study demonstrates the occurrence of non-random recruitment, indicating that the recruitment of subjects in this RDS study was not completely at random. Future studies are needed to assess the extent to which the population proportion estimates can be biased when the violation of the assumption occurs in some group traits in RDS samples.

A Model-calibration Approach to Using Complete Auxiliary Information from Stratified Sampling Survey Data

In stratified survey sampling, sometimes we have complete auxiliary information. One of the fundamental questions is how to effectively use the complete auxiliary information at the estimation stage. In this paper, we extend the model-calibration method to obtain estimators of the finite population mean by using complete auxiliary information from stratified sampling survey data. We show that the resulting estimators effectively use auxiliary information at the estimation stage and possess a number of attractive features such as asymptotically design-unbiased irrespective of the working model and approximately model-unbiased under the model. When a linear working-model is used, the resulting estimators reduce to the usual calibration estimator（or GREG）.

A Pseudo Random Number Generator under Windows Using Refined Descriptive Sampling

In this paper, we propose a software component under Windows that generates pseudo random numbers using RDS （Refined Descriptive Sampling） as required by the simulation. RDS is regarded as the best sampling method as shown in the literature. In order to validate the proposed component, its implementation is proposed on approximating integrals. The simulation results from RDS using ＂RDSRnd＂ generator were compared to those obtained using the generator ＂Rnd＂ included in the Pascal programming language under Windows. The best results are given by the proposed software component.

Estimation of Population Variance Using the Coefficient of Kurtosis and Median of an Auxiliary Variable under Simple Random Sampling

In this study we have proposed a modified ratio type estimator for population variance of the study variable y under simple random sampling without replacement making use of coefficient of kurtosis and median of an auxiliary variable x. The estimator’s properties have been derived up to first order of Taylor’s series expansion. The efficiency conditions derived theoretically under which the proposed estimator performs better than existing estimators. Empirical studies have been done using real populations to demonstrate the performance of the developed estimator in comparison with the existing estimators. The proposed estimator as illustrated by the empirical studies performs better than the existing estimators under some specified conditions i.e. it has the smallest Mean Squared Error and the highest Percentage Relative Efficiency. The developed estimator therefore is suitable to be applied to situations in which the variable of interest has a positive correlation with the auxiliary variable.

Improved Estimation of Rare Sensitive Attribute in a Stratified Sampling Using Poisson Distribution

In this study, we propose a two stage randomized response model. Improved unbiased estimators of the mean number of persons possessing a rare sensitive attribute under two different situations are proposed. The proposed estimators are evaluated using a relative efficiency comparison. It is shown that our estimators are efficient as compared to existing estimators when the parameter of rare unrelated attribute is known and in unknown case, depending on the probability of selecting a question.

Bias Correction Technique for Estimating Quantiles of Finite Populations under Simple Random Sampling without Replacement

In this paper, the problem of nonparametric estimation of finite population quantile function using multiplicative bias correction technique is considered. A robust estimator of the finite population quantile function based on multiplicative bias correction is derived with the aid of a super population model. Most studies have concentrated on kernel smoothers in the estimation of regression functions. This technique has also been applied to various methods of non-parametric estimation of the finite population quantile already under review. A major problem with the use of nonparametric kernel-based regression over a finite interval, such as the estimation of finite population quantities, is bias at boundary points. By correcting the boundary problems associated with previous model-based estimators, the multiplicative bias corrected estimator produced better results in estimating the finite population quantile function. Furthermore, the asymptotic behavior of the proposed estimators is presented. It is observed that the estimator is asymptotically unbiased and statistically consistent when certain conditions are satisfied. The simulation results show that the suggested estimator is quite well in terms of relative bias, mean squared error, and relative root mean error. As a result, the multiplicative bias corrected estimator is strongly suggested for survey sampling estimation of the finite population quantile function.