Two-phase Sampling Scheme Research Articles

Adaptive sampling with tensor leverage scores for exact low-rank third-order tensor completion

Tensor completion aims at estimating the missing entries from the incomplete observation. Under the tensor singular value decomposition framework, the exact recovery of a low-tubal-rank third-order tensor could be achieved via convex optimization with high probability if the tensor satisfies the tensor incoherence condition. In this work, we show that, when the random selection of entries is made adaptive to a distribution which is dependent on the coherence structure of the tensor, any low-tubal-rank tensor of the size n×n×n with tubal-rank r can be exactly recovered with high probability from as few as O(rn2log2⁡(n)) randomly chosen entries. In practice, tensor leverage scores are not known a priori, and we design a two-phase adaptive sampling strategy to obtain the leverage scores. Numerical experiments on synthetic and real-world third-order tensor data sets are used to validate our theoretical results and illustrate that the tensor recovery performance of the proposed two-phase adaptive sampling scheme is better than that of the other state-of-the-art methods.

A general class of improved population variance estimators under non-sampling errors using calibrated weights in stratified sampling.

This paper proposes a new calibration estimator for population variance within a stratified two-phase sampling design. It takes into account random non-response and measurement errors, specifically applying this method to estimate the variance in Gas turbine exhaust pressure data. The study integrates additional information from two highly positively correlated auxiliary variables to develop a general class of estimators tailored for the stratified two-phase sampling scheme. The properties of these estimators, in terms of their biases and mean square errors, have been thoroughly examined and extensively analyzed through numerical and simulation studies. Furthermore, the calibrated weights of the strata are derived. The proposed estimators outperform the natural estimator of population variance. Finally, suitable recommendations have been made for survey statisticians intending to apply these findings to real-life problems.

Calibration-based approach in estimation of population mean under stratified two-phase sampling scheme

Calibration-based approach in estimation of population mean under stratified two-phase sampling scheme

Quantify the Impact of Non-Response and Measurement Error of Sensitive Variable(s) under Two-Phase Sampling employing ORRT Models

Throughout this article, a two-phase sampling (TPS) technique is employed to estimate the population mean of the sensitive variable. The current article endeavours to develop a chain ratio type estimator for the estimation of sensitive variable(s) in the presence of non-response and measurement error simultaneously by utilizing ORRT models under a two-phase sampling technique. The significant aspects associated with the suggested estimator characterized by bias and mean squared error have been evaluated. Besides this, the utterance for the minimum mean squared error for the optimal values has also been identified. The supremacy of the proposed estimator has been compared with the modified existing estimators under the TPS scheme by using two sensitive auxiliary variables. To clarify the theoretical findings, a simulation study along with a hypothetical generated population and a real population which is based on abortion rates from Statistical Abstract of the United States: 2011 are also addressed in this study.

Improved estimators of population variance using robust measures in case of missing data

In this paper, an attempt has been made in proposing some improved estimators of finite population variance when random non-response is present in the study variable, on the estimators given by Sharma and Singh (2018). Four improved regression type estimators have been proposed using information on two auxiliary variables, one of which has missing values in the data, using two-phase sampling scheme. Different robust regression methods have been applied on the proposed techniques so as to improve the efficiency of the estimators in case of outliers. It has been observed that robust estimators are more efficient than the proposed ones under some conditions. All the proposed estimators are compared with existing estimators for different values of probability of non-response and the results are verified by using empirical and simulation studies.

Ratio estimation of two population means in two-phase stratified random sampling under a scrambled response situation

In this paper, we have described the development of an effective two-phase stratified random sampling estimation procedure in a scrambled response situation. Two different exponential, regression-type estimators were formed separately for different structures of two-phase stratified sampling schemes. We have studied the properties of the suggested strategy. The performance of the proposed strategy has been demonstrated through numerical evidence based on a data set of a natural population and a population generated through simulation studies. Taking into consideration the encouraging findings, suitable recommendations for survey statisticians are prepared for the application of the proposed strategy in real-life conditions.

Predictive Estimation of Finite Population Mean in Case of Missing Data Under Two-phase Sampling

The present paper deals with the problem of estimation of finite population mean of study variable using two auxiliary variables in two-phase sampling scheme using predictive approach in case of missing values of the study variable and unknown population mean of first auxiliary variable. Four classes of such estimators have been proposed using this predictive approach. The expressions of bias and mean square errors are derived up to first order of approximation. The optimal values of the constants involved in the proposed classes of estimators have been obtained and thus minimum mean square errors of the proposed classes are obtained in this study. The empirical and graphical comparisons with regression type estimators (under single phase and double phase sampling scheme) and also among themselves have been made for evaluating the performance of the proposed classes for different choices of non-responding units. Five real data sets and three simulated data sets following normal distribution have been used to evaluate the performance of the proposed classes. Numerical findings confirm the theoretical results obtained regarding superiority of proposed classes of estimators over the conventional regression type estimators in terms of percent relative efficiencies.

Modified Classes of Regression-Type Estimators of Population Mean in the Presence of Auxiliary Attribute under Double Sampling Scheme

In sample survey, reliable and efficient estimates are often obtained using information from auxiliary variables during estimation and designing stages. However, there are times when the auxiliary information is attribute-based. Some authors have proposed estimators using auxiliary attribute information when the population mean of auxiliary attribute is unknown. However, the estimators are ratio- based estimators which are less efficient when the bi-serial correlation between the study variable and the auxiliary attribute is negative. In this study, regression approach was used to modify estimator d ZKi t to produce estimators that can be used for both negative and positive correlation. In addition, another existing estimator was also modified to produce an estimator that is independent of an unknown population parameter. The Biases and Mean Squared Errors (MSEs) of the modified estimators were determined using the Taylor series approach up to the first order of approximation. The proposed estimators’ efficiency conditions over some existing estimators were established. Empirical investigations were done using stimulation study and the results revealed that proposed estimators have the lowest MSEs and the highest PREs of all the competing estimators and therefore can give better estimates of the population mean. Therefore, it can be concluded that proposed estimators have better predictive power for estimating population mean under two-phase sampling scheme.

Chain ratio and product estimators for population mode using two-phase sampling scheme

Background: For skewed datasets, the mode is utilized as a more appropriate measure of location. We formed chain ratio and product estimators for the population mode using two types of auxiliary information under the two-phase sampling scheme. Methods: Expressions for biases and mean square errors for the formed estimators up to the first order of approximation are obtained. The confidence intervals of the estimators are obtained and the aspects related to fixed cost and fixed variance are also studied. A simulation study is performed to support the theoretical outcomes. A real dataset is also provided which was collected from the department of agriculture, United States. Results: The simulation results for the fixed first-phase sample size and the second-phase sample size show that the mean square error is 0.035, the bias is 0.012, the confidence interval is 2.67-3.15, and the cost of the survey under the fixed variance is ₹ 194.67 of the proposed estimator , which is lower than 0.039, 0.055, 2.70-3.20, and ₹ 247.37 of the ratio estimator and 0.078, 0.041, 2.57-3.31, and ₹ 268.73 of the naive estimator . Conclusions: The simulation results show that the proposed estimator performs better than the ratio estimator and the naive estimator .

Modelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information

• A suitable GEDI RH metric was selected to represent NFI dominant height at plot level. • A method that pairs up NFI plots with nearby GEDI footprints up to a critical distance suggested by the semi-variance examination. • Small area estimation of forest volume was improved with the help of GEDI auxiliary data. The French National Forest Inventory provides detailed forest information up to large national and regional scales. Forest inventory for small areas of interest within a large population is equally important for decision making, such as for local forest planning and management purposes. However, sampling these small areas with sufficient ground plots is often not cost efficient. In response, small area estimation has gained increasing popularity in forest inventory. It consists of a set of techniques that enables predictions of forest attributes of subpopulation with the help of auxiliary information that compensates for the small field samples. Common sources of auxiliary information usually come from remote sensing technology, such as airborne laser scanning and satellite imagery. The newly launched NASA’s Global Ecosystem Dynamics Investigation (GEDI), a full waveform Lidar instrument, provides an unprecedented opportunity of collecting large-scale and dense forest sample plots given its sampling frequency and spatial coverage. However, the geolocation uncertainty associated with GEDI footprints create important challenges for their use for small area estimations. In this study, we designed a process that provides NFI measurements at plot level with GEDI auxiliary information from nearby footprints. We demonstrated that GEDI RH 98 is equivalent to NFI dominant height at plot level. We stressed the importance of pairing NFI plots with nearby GEDI footprints, based on not only the distance in between but also their similarities, i.e., forest heights and forest types. Subsequently, these NFI-GEDI pairs were used for small area estimations following a two-phase sampling scheme. We showcased that, with an adequate sample size, small area estimation with GEDI auxiliary data can improve the accuracy of forest volume estimates.

A sampling strategy for assessing habitat coverage at a broad spatial scale

• We develop an adaptive monitoring approach, to estimate the coverage of habitat types using a two-phase sampling scheme. • The adaptive monitoring approach demonstrated high reliability in a simulation study performed on the whole country of Italy. • The method is cost effective, adaptable across habitat types, and transferable to other countries or whole European Union. The quantitative assessment of habitat conservation status is a major task for European Union member states in compliance with Council Directive 92/43. One goal of the European 2030 Biodiversity Strategy is the effective management of habitats that show declining trends. While various approaches have been adopted for national assessments, there is no consensus on how to achieve common statistically sound estimates of the criteria indicated by the EU Directive for the evaluation of the status and trend of habitat types. Here, we present an adaptive monitoring approach based on a two-phase sampling scheme to estimate the coverage of EU terrestrial habitat types, which is one of the four criteria indicated by the Habitats Directive. We used 9 habitats distributed among different EU member states choosing Italy as a case study. The development of the methodological approach is described, and a simulation study was performed to check the precision of the coverage estimators accounting for the lack of sampled data (nonresponse treatment), subregions and sustainable sampling effort. We found that our two-phase sampling approach has the potential to increase precision in estimating the coverage of habitat types (approximated at 1 ha cell size) with respect to the precision achieved by simple random sampling without replacement, which is the simplest sampling approach. Adopting a small sampling fraction ( ⩽ 0.04 % ) of the survey area, the relative standard errors ranged from 7 to 15 % for common habitats whose presence is strongly correlated with the habitat suitability scores furnished by an expert team. In the challenging context of a “mandated” monitoring type, our approach provides sound statistical estimates of habitat coverage with the possibility of applying a standardised and transferable sampling scheme that is easily repeatable over time.

Acceptability of Overdose Prevention Sites in the Business Community in Baltimore, Maryland

Intervetions are urgently needed to reduce the trajectory of the US opioid overdose epidemic, yet implementation is often hampered by resistance or opposition from key community stakeholders. While businesses are economically and physically impacted by the opioid epidemic, they are rarely engaged in efforts to reduce its impact. The establishment of overdose prevention sites (OPS) is being discussed throughout many US jurisdictions with limited attention to the potential positive role of businesses in that process. We surveyed business owners and employees of businesses located in neighborhoods with concentrated drug markets. The study’s primary aim was to examine their attitudes to locally-placed OPS. An iterative, two-phase sampling strategy was used to identify recruitment zones. In person (December 2019–March 2020) and telephone-based (April–July 2020) surveys were administered to distinct business owners and employees (N = 149). Sixty-five percent of participants supported OPS in their neighborhood and 47% had recently witnessed an overdose in or around their workplace. While 70% had heard of naloxone, and 38% reported having it on the premises. Correlates of supporting an OPS locally included living in the same neighborhood as work (adjusted odds ratio (aOR) 1.99, 95% confidence intervals (CI): 1.30–3.05); having a more positive attitude towards people who use drugs (aOR 1.33, 95% CI: 1.13–1.58); and having recently seen an overdose in/around the workplace (aOR 2.86, 95% CI: 1.11–7.32). Lack of support being an owner (aOR 0.35, 95% CI: 0.15–0.83). These data indicate the extent to which businesses are directly impacted by the opioid epidemic and the power of personal experience in shaping OPS support in advocacy efforts.

A new class of ratio type estimators in single- and two-phase sampling

<abstract> <p>The estimation of a certain population characteristics is required for several situations. The estimates are built so that the error of estimation is minimized. In several situations estimation of the population mean is required. Different estimators for the mean are available but, there is still room for improvement. In this paper, a new class of ratio-type estimators is proposed for the estimation of the population mean. The estimators are proposed for single- and two-phase sampling schemes. The expressions for bias and mean square error are obtained for single-phase and two-phase sampling estimators. Mathematical comparison of the proposed estimators has been achieved by using some existing single-phase and two-phase sampling estimators. Extensive simulations have been conducted to compare the proposed estimators with some available single- and two-phase sampling estimators. It has been observed that the proposed estimators are better than the existing estimators. Consequently, the proposed ratio estimators are recommended for use by the practitioners in various fields of industry, engineering and medical and physical sciences.</p> </abstract>

Some Improved Estimators of Population Mean using Two-Phase Sampling Scheme in the Presence of Non-Response

In the present paper, we have proposed some improved estimators of the population mean utilizing the information on two auxiliary variables adopting the idea of two-phase sampling under non-response. In order to propose the estimators, we have assumed that the study variable and first auxiliary variable suffer from non-response while the second (additional) auxiliary variable is free from non-response. We have derived the expressions for biases and mean square errors of the proposed estimators and compared them with that of usual estimator and some well known existing estimators of the population mean. The theoretical results have also been illustrated with some empirical data.

How to increase the joint provision of ecosystem services by agricultural systems. Evidence from coffee-based agroforestry systems

CONTEXTAgricultural systems can provide ecosystem services (ES) beneficial for both the sustainability of farms and the quality of life for humans. Agriculture is regularly criticized for focusing technical management of cropping systems more on production services than on support or regulation services. To achieve the agroecological intensification of cropping systems, a joint provision of multiple ES is required. OBJECTIVEOur aim was to (i) understand the determinants of the provision of ES and (ii) analyze the relationships between these services in order to (iii) identify agroecological intensification pathways. We focused our study on four ES, which are (1) coffee production, (2) water quality preservation, (3) carbon sequestration and (4) biodiversity conservation, provided by coffee agroforestry systems in a small region in Nicaragua. METHODSA two-phase sampling scheme was implemented to measure and elucidate the provision of these services. First, we selected a large sample (82 coffee plots) to gain insight into and quantify the four ES. Secondly, we extracted a sub-sample (27 plots) showing variability in the provision of the four ES, to closely examine the determinants of the service most useful to farmers, coffee production. RESULTS AND CONCLUSIONThe results showed that carbon sequestration (in average 36 t.ha−1.yr−1) was not correlated with coffee yield (in average 1127 kg.ha−1.yr−1) and depended more on the presence of a few big trees in farm plots (Ø > 0.9 m) than on tree density. Yield increased with tree biodiversity up to a threshold (Shindex = 1.5), after which it clearly declined. The use of the most harmful pesticides to human health at higher doses than recommended did not lead to the highest yields. The most important determinants of coffee production were soil nitrogen content, soil pH, solar radiation, disease and weed incidence. Although reducing the shade tree density increased coffee production, this reduction was not necessarily related to a decrease in shade tree biodiversity and carbon sequestration, or an increase in water contamination potential. A few farmers actually achieved such high joint ES provision, in particular by selecting adequate shade trees grown at moderate densities. SIGNIFICANCEThe novelty of this article lies on an original method that consists in analyzing the ES provided by cropping systems in order to identify management strategies that are effective in providing a higher combined level of ES than those currently provided. We emphasize the importance of linking agricultural practices to the ES delivered, in order to gain an in-depth understanding of which technical levers are positively correlated with the determinants of the expected services.

Calibration estimator of population mean under stratified two-phase sampling using two auxiliary variables

In the present paper, we have tried to discover a new calibration estimator of the population mean under stratified two-phase sampling design utilizing the information on two auxiliary variables. In order to develop the calibration estimator, we have obtained new optimum stratum weights so that the MSE/Variance of the estimator would be minimized. Moreover, we have compared the efficiency of the proposed calibration estimator with that of the calibration estimator considered under the stratified two-phase sampling scheme using single auxiliary variable. An empirical illustration along with a simulation analysis has also been presented to demonstrate the theoretical results.

Generalized Ratio-cum-Product Estimator for Finite Population Mean under Two-Phase Sampling Scheme

A method to lower the MSE of a proposed estimator relative to the MSE of the linear regression estimator under two-phase sampling scheme is developed. Estimators are developed to estimate the mean of the variate under study with the help of auxiliary variate (which are unknown but it can be accessed conveniently and economically). The mean square errors equations are obtained for the proposed estimators. In addition, optimal sample sizes are obtained under the given cost function. The comparison study has been done to set up conditions for which developed estimators are more effective than other estimators with novelty. The empirical study is also performed to supplement the claim that the developed estimators are more efficient.

Generalized Class of Estimators Using Dual of Auxiliary Variable Under Non-Response

The present study considers the problem of estimating the population mean of study variable by making the dual use of an auxiliary information in the presence of non-response. We have proposed an improved general family of estimators using an auxiliary variable as well as its rank under simple random sampling without replacement (SRSWOR) and two-phase sampling scheme. We have shown the effective performance of proposed class of estimators over usual unbiased, ratio, regression and difference estimators, Singh and Kumar’s (Austral N Z Stat 50(4):395–402, 2008) estimator, Riaz and Shabbir (Pak J Stat, 30(1):83–100, 2014), Yaqub et al.’s (Commun Stat Theory Methods 46(24):12130–12151, 2017) estimator and Irfan et al.’s (Sci Iran, 2020. https://doi.org/10.24200/SCI.2020.53592.3318 ) estimator under SRSWOR and the works: Singh and Kumar’s (Stat Oper Res Trans 33(1):71–84, 2009; Stat Pap 51:559–402, 2010), Kumar’s (Commun Stat Appl Methods 19(5):663–671, 2012), Riaz and Shabbir’s (Pak J Stat, 30(1):83–100, 2014), Yaqub et al.’s (Commun Stat Theory Methods 46(24):12130–12151, 2017) and Irfan et al.’s (Sci Iran, 2020. https://doi.org/10.24200/SCI.2020.53592.3318 ) estimators under two-phase sampling using an empirical study. A simulation study is also carried out in support of our objectives.

A combination sampling approach for epidemiologic research in humanitarian settings: a case analysis of a study of depressive disorder prevalence among refugees in Greece

BackgroundUnderstanding the burden of common mental health disorders, such as depressive disorder, is the first step in strengthening prevention and treatment in humanitarian emergencies. However, simple random sampling methods may lead to a high risk of coercion in settings characterized by a lack of distinction between researchers and aid organizations, mistrust, privacy concerns, and the overarching power differential between researchers and populations affected by crises. This case analysis describes a sampling approach developed for a survey study of depressive disorder in a Syrian refugee camp in Greece (n = 135).DiscussionSyrian refugees face an extraordinarily high burden of depressive disorder during the asylum process (43%), necessitating population screening, prevention, and treatment. In order to preserve the informed consent process in this refugee camp setting, the research team developed a two-phase sampling strategy using a map depicting the geographical layout of the housing units within the camp. In the first phase, camp management announced a research study was being undertaken and individuals were invited to volunteer to participate. The participants’ container (housing) numbers were recorded on the map, but were not linked to the survey data. Then, in the second phase, the camp map was used for complementary sampling to reach a sample sufficient for statistical analysis. As a result of the two phases of the sampling exercise, all eligible adults from half the containers in each block were recruited, producing a systematic, age- and sex-representative sample.ConclusionsCombining sampling procedures in humanitarian emergencies can reduce the risk of coerced consent and bias by allowing participants to approach researchers in the first phase, with a second phase of sampling conducted to recruit a systematic sample. This case analysis illuminates the feasibility of a two-phase sampling approach for drawing a quasi-random, representative sample in a refugee camp setting.

An Improvement in Estimation of Population Mean using Two Auxiliary Variables and Two- Phase Sampling Scheme under Non-Response

In the present paper, we have proposed some improved ratio and regression-type estimators of the finite population mean utilizing the information on two auxiliary variables in the presence of non-response. The two-phase sampling scheme has been used to accomplish the job of estimating the desired parameter. The expressions for the basic properties such as bias and mean square error (MSE) of the proposed estimators have been derived up to the first order of approximation. A comparative study of the proposed estimators with some existing estimators has also been carried out through a real data set.