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Declining response rates and increasing costs have led to greater use of non-probability samples in election polling. But non-probability samples may suffer from selection bias due to differential access, degrees of interest and other factors. Here we estimate voting preference for 19 elections in the US 2014 midterm elections by using large non-probability surveys obtained from SurveyMonkey users, calibrated to estimated control totals using model-assisted calibration combined with adaptive LASSO regression, or the estimated controlled LASSO, ECLASSO. Comparing the bias and root-mean-square error of ECLASSO with traditional calibration methods shows that ECLASSO can be a powerful method for adjusting non-probability surveys even when only a small sample is available from a probability survey. The methodology proposed has potentially broad application across social science and health research, as response rates for probability samples decline and access to non-probability samples increases.

Increasing costs and non-response rates of probability samples have provoked the extensive use of non-probability samples. However, non-probability samples are subject to selection bias, resulting in difficulty for inference. Calibration is a popular method to reduce selection bias in nonprobability samples. When rich covariate information is available, a key problem is how to select covariates and estimate parameters in calibration for non-probability samples. In this paper, the model-assisted SCAD calibration is proposed to make population inference from non-probability samples. A parametric model between the study variable and covariates is first established. SCAD is then used to estimate the model parameters based on non-probability samples. The modified forward Kullback—Leibler distance is lastly explored to conduct calibration for non-probability samples based on the estimated parametric model. The theoretical properties of the modelassisted SCAD calibration estimator are further derived. Results from simulation studies show that the model-assisted SCAD calibration estimator yields the smallest bias andmean square error compared with other estimators. Also, a real data from the 2017 Netizen Social Awareness Survey (NSAS) is used to demonstrate the proposed methodology.

Periodic assessment of forest aboveground biomass (AGB) is essential to regulate the impacts of the changing climate. However, AGB estimation using field-based sample survey (FBSS) has limited precision due to cost and accessibility constraints. Fortunately, remote sensing technologies assist to improve AGB estimation precisions. Thus, this study assessed the role of remotely sensed (RS) data in improving the precision of AGB estimation in an Afromontane forest in south-central Ethiopia. The research objectives were to identify RS variables that are useful for estimating AGB and evaluate the extent of improvement in the precision of the remote sensing-assisted AGB estimates beyond the precision of a pure FBSS. Reference AGB data for model calibration and estimation were collected from 111 systematically distributed circular sample plots (SPs) of 1000 m2 area. Independent variables were derived from Landsat-8, Sentinel-2 and PlanetScope images acquired in January 2019. The area-weighted mean and standard deviation of the spectral reflectance, spectral index and texture (only for PlanetScope) variables were extracted for each SP. A maximum of two independent variables from each image type was fitted to a generalized linear model for AGB estimation using model-assisted estimators. The results of this study revealed that the Landsat-8 model with the predictor variable of shortwave infrared band reflectance and the PlanetScope model with the predictor variable of green band reflectance had estimation efficiency of 1.40 and 1.37, respectively. Similarly, the Sentinel-2 model, which had predictor variables of shortwave infrared reflectance and standard deviation of green leaf index, improved AGB estimation with the relative efficiency of 1.68. Utilizing freely available Sentinel-2 data seems to enhance the AGB estimation efficiency and reduce cost and extensive fieldwork in inaccessible areas.

Surveys usually include questions where individuals must select one in a series of possible options that can be sorted. On the other hand, multiple frame surveys are becoming a widely used method to decrease bias due to undercoverage of the target population. In this work, we propose statistical techniques for handling ordinal data coming from a multiple frame survey using complex sampling designs and auxiliary information. Our aim is to estimate proportions when the variable of interest has ordinal outcomes. Two estimators are constructed following model-assisted generalised regression and model calibration techniques. Theoretical properties are investigated for these estimators. Simulation studies with different sampling procedures are considered to evaluate the performance of the proposed estimators in finite size samples. An application to a real survey on opinions towards immigration is also included.

In survey sampling, calibration is a popular tool used to make total estimators consistent with known totals of auxiliary variables and to reduce variance. When the number of auxiliary variables is large, calibration on all the variables may lead to estimators of totals whose mean squared error (MSE) is larger than the MSE of the Horvitz-Thompson estimator even if this simple estimator does not take account of the available auxiliary information. We study a new technique based on dimension reduction through principal components that can be useful in this large dimension context. Calibration is performed on the first principal components, which can be viewed as the synthetic variables containing the most important part of the variability of the auxiliary variables. When some auxiliary variables play a more important role than others, the method can be adapted to provide an exact calibration on these variables. Some asymptotic properties are given in which the number of variables is allowed to tend to infinity with the population size. A datadriven selection criterion of the number of principal components ensuring that all the sampling weights remain positive is discussed. The methodology of the paper is illustrated, in a multipurpose context, by an application to the estimation of electricity consumption with the help of 336 auxiliary variables.

The Model assisted estimators are approximately design unbiased, consistent and provides robustness in the case of large sample sizes. The model assisted estimators result in reduction of the design variance if underlying model reasonably defines the regression relationship.  If the model is misspecified, then model assisted estimators might result in an increase of the design variance but remain approximately design unbiased and show robustness against model-misspecification. The well-known model assisted estimators, generalized regression estimators are members of a larger class of calibration estimators. Calibration method generates calibration weights that meet the calibration constraints and have minimum distance from the sampling design weights. By using different distance measures, classical calibration approach generates different calibration estimators but with asymptotically identical properties. The constraint of distance minimization was reduced for studying the properties of calibration estimators by proposing a simple functional form approach. The approach generates calibration weights that prove helpful to control the changes in calibration weights by using different choices of auxiliary variable’s functions.  This paper is an extended work on model assisted approach by using functional form of calibration weights. Some new model assisted estimators are considered to get efficient and stabilized regression weights by introducing a control matrix. The asymptotic un-biasedness of the proposed estimators is verified and the expressions for MSE are derived in three different cases.  A simulation study is done to compare and evaluate the efficiency of the proposed estimators with some existing model assisted estimators.

Currently, high precision estimation of non-linear parameters such as Gini indices, low income proportions or other measures of inequality is particularly crucial. We propose a general class of estimators for such parameters that take into account univariate auxiliary information assumed to be known for every unit in the population. Through a non-parametric model-assisted approach, we construct a unique system of survey weights that can be used to estimate any non-linear parameter that is associated with any study variable of the survey, using a plug-in principle. Based on a rigorous functional approach and a linearization principle, the asymptotic variance of the estimators proposed is derived, and variance estimators are shown to be consistent under mild assumptions. The theory is fully detailed for penalized B-spline estimators together with suggestions for practical implementation and guidelines for choosing the smoothing parameters. The validity of the method is demonstrated on data extracted from the French Labour Force Survey. Point and confidence interval estimation for the Gini index and the low income proportion are derived. Theoretical and empirical results highlight our interest in using a non-parametric versus a parametric approach when estimating non-linear parameters in the presence of auxiliary information.

Linear mixed models cover a wide range of statistical methods, which have found many uses in the estimation for complex surveys. The purpose of this work is to consider methods by which linear mixed models may be used at the design stage of a survey to incorporate available auxiliary information. This paper reviews the ideas of balanced sampling and the cube algorithm, and proposes an implementation of the latter by which penalized balanced samples can be selected. Such samples can reduce or eliminate the need for linear mixed model weight adjustments, a result demonstrated theoretically and via simulation. Horvitz-Thompson estimators for such samples will be highly efficient for any responses well approximated by a linear mixed model in the auxiliary information. In Monte Carlo experiments using nonparametric and temporal linear mixed models, the strategy of penalized balanced sampling with Horvitz-Thompson estimation dominates a variety of standard strategies.

Enzyme-catalyzed chemical reactions produce heat. We developed an enclosed, capillary-perfused nanocalorimeter platform for thermometric enzyme-linked immunosorbent assay (TELISA). We used catalase as enzymes to model the thermal characteristics of the micromachined calorimeter. Model-assisted signal analysis was used to calibrate the nanocalorimeter and to determine reagent diffusion, enzyme kinetics, and enzyme concentration. The model-simulated signal closely followed the experimental signal after selecting for the enzyme turnover rate (kcat) and the inactivation factor (InF), using a known label enzyme amount (Ea). Over four discrete runs (n = 4), the minimized model root mean square error (RMSE) returned 1.80 ± 0.54 fmol for the 1.5 fmol experiments, and 1.04 ± 0.37 fmol for the 1 fmol experiments. Determination of enzyme parameters through calibration is a necessary step to track changing enzyme kinetic characteristics and improves on previous methods to determine label enzyme amounts on the calorimeter platform. The results obtained using model-system signal analysis for calibration led to significantly improved nanocalorimeter platform performance.

Remote sensing provides timely, economic, and objective data over a large area and has become the main data source for land cover/use area estimation. However, the classification results cannot be directly used to calculate the area of a given land cover/use type because of classification errors. The main purpose of this study is to explore the performance and stability of several model-assisted estimators in various overall accuracies of classification and sampling fractions. In this study, the confusion matrix calibration direct estimator, confusion matrix calibration inverse estimator, ratio estimator, and simple regression estimator were implemented to infer the areas of several land cover classes using simple random sampling without replacement in two experiments: a case study using simulation data based on RapidEye images and that using actual RapidEye and Huan Jing (HJ)-1A images. In addition, the simple estimator using a simple random sample without replacement was regarded as a basic estimator. The comparison results suggested that the confusion matrix calibration estimators, ratio estimator, and simple regression estimator could provide more accurate and stable estimates than the simple random sampling estimator. In addition, high-quality classification data played a positive role in the estimation, and the confusion matrix inverse estimators were more sensitive to the classification accuracy. In the simulated experiment, the average deviation of a confusion matrix calibration inverse estimator decreased by about 0.195 with the increasing overall accuracy of classification; otherwise, the variation of the other three model-assisted estimators was 0.102. Moreover, the simple regression estimator was slightly superior to the confusion matrix calibration estimators and required fewer sample units under acceptable classification accuracy levels of 70%–90%.