Search or ask a question

What is the huber/white/sandwich estimator advantageous for large sample survey?

Frequentist inference

Consistent estimator

Minimum-variance unbiased estimator

Bias of an estimator

Best insight from top research papers

The Huber-White sandwich estimator is advantageous for large sample surveys because it provides unbiased standard error estimates for regression coefficients even when the covariance structure is misspecified. It performs optimally in balanced designs with a large number of participants and few repeated measurements. However, its asymptotic properties do not hold in small sample settings, leading to biased downwards estimates of variances. To address this deficiency, modified forms of the sandwich estimator have been proposed, such as the one by Liang and Zeger . These modified estimators aim to correct the bias and improve the accuracy of standard error estimation in small sample and rare-event settings. The performance of these modified sandwich estimators has been compared to the traditional Liang-Zeger estimator and alternative forms proposed by other authors, such as Morel, Pan, and Mancl-DeRouen .

Answers from top 4 papers

PDF

Open Access

More filters

Papers (4)	Insight
Open access•Journal Article•DOI Estimating the variance in a pseudo-observation scheme with competing risks Morten Overgaard, Erik T. Parner, Jan Skov Pedersen - Show less +2 more 01 Dec 2018-Scandinavian Journal of Statistics 10 Citations	The Huber-White sandwich estimator is advantageous for large sample surveys because it reaches the right level and avoids bias compared to the proposed variance estimator.
Journal Article•DOI Sandwich Estimation for Multi-Unit Reporting on a Stratified Heterogeneous Surface Jinfeng Wang, Robert Haining, Tiejun Liu, Lianfa Li, Chengsheng Jiang - Show less +4 more 01 Jan 2013-Environment and Planning A 38 Citations	The provided paper does not mention the Huber/White/Sandwich estimator.
Open access•Journal Article•DOI Modification of the Sandwich Estimator in Generalized Estimating Equations with Correlated Binary Outcomes in Rare Event and Small Sample Settings Paul Rogers, Julie A. Stoner - Show less +1 more 02 Feb 2018-American Journal of Applied Mathematics and Statistics 16 Citations	The paper does not mention the Huber/White/Sandwich estimator. The paper is about the modification of the sandwich estimator in generalized estimating equations with correlated binary outcomes in rare event and small sample settings.
Open access•Posted Content•DOI A Bayesian 'sandwich' for variance estimation and hypothesis testing 30 Jun 2022	The provided paper does not mention the Huber/White/sandwich estimator or its advantages for large sample surveys.

My columns

Related Questions

What is purposive sampling technique and advantage in selecting respondents?5 answersPurposive sampling is a non-probability sampling method that involves selecting individuals based on specific characteristics or expertise relevant to the research topic. This technique is particularly useful when studying a specific cultural domain or when targeting knowledgeable experts within a population. The advantage of purposive sampling lies in its efficiency and robustness, even when compared to random probability sampling methods. By carefully choosing participants based on the study's purpose, researchers can ensure that each selected individual provides unique and valuable information, enhancing the quality of data gathered. This method allows for the collection of in-depth insights from experts, contributing to the richness and depth of the research findings.

Why do the majority of survey participants believe in the importance and benefits of the Envelope method?4 answersThe majority of survey participants believe in the importance and benefits of the Envelope method due to its efficiency in dimension reduction for various models, particularly in multivariate parameter estimation. The Envelope method offers advantages in analyzing data with highly correlated variables and provides a more straightforward approach compared to iterative optimization algorithms, especially in high-dimensional data scenarios. Additionally, the Envelope method, such as the Non-Iterative Envelope Component Estimation (NIECE), bridges the gap between envelope methods and principal components in regression, offering significant computational advantages and improved standard methods in linear and generalized linear models. This method's simplicity and computational efficiency make it a valuable tool for various applications, enhancing its perceived importance and benefits among survey participants.

Why is sampling resorted to in survey research?4 answersSampling is utilized in survey research to efficiently study a subset of a population, enabling cost-effective and timely data collection while ensuring representativeness. By selecting a sample from a larger population, researchers can draw accurate inferences about the entire group. This method is crucial when studying attributes like disease prevalence, where studying the entire population is impractical. Sampling techniques, such as probability sampling, help control statistical properties of estimators, ensuring the findings are reliable and applicable to the broader population. Additionally, sampling allows for the integration of multiple data sources, enhancing the robustness and efficiency of inference in survey research.

What are the advantages and disadvantages of using different survey data collection techniques?5 answersSurvey data collection techniques offer various advantages and disadvantages. Structured questionnaire surveys provide a systematic approach to gathering data, translating research questions effectively, and ensuring data quality. Surveys, in general, allow for economical access to participants online, aiding in knowledge acquisition and policy-making, but they often suffer from low response rates. Supplementing survey data with mobile technologies can enhance data collection by offering new opportunities, although challenges like respondent burden and device effects may arise. Additionally, differences in sampling design and response rates can impact the accuracy and comparability of data collected from religious congregations, emphasizing the importance of high response rates for accurate estimates. Online surveys, while convenient, face criticisms such as survey and item nonresponse bias.

What is the purpose of sample surveys?5 answersThe purpose of sample surveys is to gather data from a subset of a larger population in order to make inferences about the entire population. Sample surveys are commonly used in educational research to investigate various topics such as academic performance, characteristics of educators, societal views about education, and associations between student learning and family or community factors. These surveys are essential in academic, government, and commercial sectors to gain insights about diverse populations without needing to interview every single member of the population. Survey sampling involves selecting a random sample of units to represent a larger population, ensuring that the statistical properties of the estimators produced are controlled. By carefully designing questionnaires and following proper sampling plans, survey results can be more accurate than conducting a census of an entire population.

How to decide how many samples to collect in a survey?4 answersDetermining the appropriate sample size for a survey is a crucial aspect of the survey design. It impacts the cost, efficiency, and reliability of the statistics obtained. There is no one-size-fits-all solution for determining sample size, as it involves a trade-off between precision requirements and operational constraints such as budget, resources, and time. Various tools and methods can be used to determine sample size, such as Chebyshev's inequality, desired survey objectives, and the design effect for multi-strata surveys. These methods consider factors like the desired level of precision, the variability of the population, and the number of geographic subdivisions. Ultimately, the determination of sample size is a process of compromise that requires careful consideration of statistical requirements and practical constraints.

See what other people are reading

Role of pre test in mathematics?

Pre-tests in mathematics play a crucial role in assessing students' foundational knowledge before delving into course material. They serve as tools to determine existing subject understanding, aiding in predicting student achievement. Additionally, pre-tests can be utilized for estimating parameters in regression models and scale parameters in statistical distributions, enhancing predictive accuracy and risk analysis. Surprisingly, pre-tests covering unfamiliar material can motivate students and guide them throughout the course, leading to improved performance. Moreover, in the realm of pre-service mathematics teacher training, pre-tests are instrumental in designing high-quality test instruments through Assessment as Learning practices, enabling error detection and quality control. Overall, pre-tests in mathematics serve as diagnostic tools, predictors of success, and aids in enhancing teaching methodologies.Where are the research gaps in prognostic health management?

Research gaps in prognostic health management include concerns about the methods, tools, metrics, and standardization, which limit the applicability and industry adoption of PHM. Uncertainty quantification and management are crucial aspects in prognostics, with different interpretations for testing-based and condition-based health management. While both frequentist and Bayesian approaches are applicable in testing-based scenarios, only the Bayesian approach is suitable for condition-based health management. Additionally, the estimation of remaining useful life is more meaningful in condition-based monitoring, posing an uncertainty propagation challenge that needs to be addressed. These gaps highlight the need for further research to enhance the effectiveness and reliability of prognostic health management systems.How to use Gpower to calculate sample size?

To calculate sample size using G*Power software, researchers need to establish research goals, hypotheses, and choose appropriate statistical tests. Input the required variables such as null and alternative hypothesis, effect size, power, alpha, type I and type II errors, and select the statistical method (F, t, χ2, Z, or exact tests). G*Power simplifies the process by offering an easy-to-use and free platform for sample size and power calculations across various statistical methods. Additionally, a Shiny web app in R has been developed to compute the optimal sample size based on classical Bayesian or fully Bayesian approaches, ensuring reproducibility of results in single-arm trials with Poisson data. Overall, G*Power and Bayesian approaches provide flexibility and accuracy in determining sample sizes for research studies.What is the scientific consensus on biperiden dependence?

The scientific consensus on biperiden dependence indicates that individuals with bipolar disorder are at a significantly higher risk of substance dependence, with around 40% of bipolar subjects being dependent on substances. Moreover, there is a notable presence of addictive comorbidity independent of bipolar episodes, suggesting a separate addictive component in these individuals. In the realm of dyadic business relationships, measuring and evaluating dependence between actors is crucial. While traditional unidirectional approaches are common, a bi-directional perspective is sometimes necessary to fully grasp the mutual dependence between two parties. When studying synchronization in daily interactions, the interpretation of association values can be challenging due to dependencies in the data. Models and permutation-based frameworks are proposed to test the significance of associations while accounting for auto-dependencies, ensuring control over Type I error rates and maintaining good power.Which one is best to determine a threshold from a ROC curve: classification accuracy or Youden's index?

Youden's index is considered superior to classification accuracy for determining a threshold from a ROC curve. While classification accuracy focuses on maximizing overall correct classifications, Youden's index optimizes the trade-off between sensitivity and specificity, providing a more balanced approach. Additionally, the enhanced Youden's index with net benefit offers a method for optimal-threshold determination in shared decision making, emphasizing the maximization of patients' net benefit. Moreover, the partial Youden index is introduced as a new summary index for the ROC curve, particularly useful when focusing on specific regions of interest in clinical practice. Nonparametric predictive inference (NPI) methods have also shown promising results in selecting optimal thresholds, surpassing some limitations of traditional Youden's index in predictive performance assessment.What is strip plot design?

A strip-plot design is a type of experimental design commonly utilized in situations where factors are challenging to change, especially in multi-stage processes. It is particularly beneficial when the production process involves two or more stages with hard-to-change factors, allowing the application of the second stage to semi-finished products from the first stage. Strip-plot designs are cost-effective and statistically efficient, making them preferable over other design options like split-split-plot designs in certain scenarios. These designs are structured to handle interactions between factors, focusing on the interaction effects rather than individual main effects. Optimal design methodologies can be employed to create a variety of strip-plot designs, enhancing the efficiency and effectiveness of experimentation in such complex settings.What factors should be associated with high shannon diversity index?

High Shannon diversity index in various contexts is associated with specific factors. In breast cancer, a high Shannon index of gene copy number variation is linked to adverse pathologic features like high histologic grade, p53 overexpression, and negative hormone receptor status, indicating intratumoral heterogeneity. In community structures, high dominance scenarios and community structures similar to simulated ones show better performance in estimating Shannon's diversity using the bootstrap-t technique. In genetic studies, the original Shannon index is negatively biased at small sample sizes, and unbiased estimators like Zahl's are recommended for accurate diversity measures. Lastly, in environmental studies, the Shannon index around a paper mill reflects air pollution levels, with lower values indicating higher pollution levels closer to industrial areas.What is winsorization?

Winsorization is a statistical technique used to address outliers in data by replacing extreme values with less extreme ones. It involves trimming or capping the extreme values of a dataset to mitigate their impact on statistical analysis. This method is particularly useful in scenarios where data may contain errors or outliers, such as in sheep farm data or when dealing with noisy observations in neural networks. Winsorization has been shown to improve model performance, reduce bias introduced by outliers, and enhance the accuracy of estimations in various fields, including machine learning, composite indicator ranking, and importance sampling estimation.How do statistical methods aid in the analysis and interpretation of biological data?

Statistical methods play a crucial role in analyzing and interpreting biological data by providing a framework to draw meaningful conclusions from experimental results. Different paradigms such as Frequentist, Bayesian, likelihood, and modeling approaches are utilized to study various aspects of biological phenomena. These methods help in handling experimental errors and making informed decisions based on available data in biosciences. In genetic research, statistical models like Least Absolute Shrinkage and Selection Operator Regression, Chen-Stein Method, and Logistic Regression aid in addressing reproducibility issues and offer effective data analysis tools. Furthermore, logistic regression is commonly used in genetic data analysis to predict outcomes based on predictor variables, enhancing the accuracy of results in veterinary and biological practices. The integration of biostatistics and bioinformatics provides essential support for analyzing microbial ecology and molecular biology data, aiding researchers in interpreting scientific findings effectively.What test is usually the alternative for t-test when assumptions are violated?

When the assumptions of the t-test are violated, the nonparametric Wilcoxon test is often recommended as an alternative. This recommendation stems from the robustness and power properties of the Wilcoxon test under various non-normal distributions, especially when dealing with small sample sizes and skewed data. Additionally, the Wilcoxon test is known to be less affected by negative between-group correlations compared to the paired t-test, making it a suitable alternative in such cases. Furthermore, in scenarios where the classical t-tests tend to be conservative or liberal due to non-normality, the Wilcoxon tests, specifically the Wilcoxon signed-rank and rank-sum tests, are preferred alternatives. Overall, the Wilcoxon test emerges as a robust choice when the assumptions of the t-test are not met.What does the reason why happen when the means is getting lower than the control.?

When the means in an analysis of variance are lower than the control due to variations, the test may exhibit a downward bias, affecting its power function. This downward bias can impact the statistical control of experiments and influence the accuracy of the analysis. In such situations, temporary shortages or surpluses may occur in the market, leading to self-correction mechanisms. Additionally, in control systems, the ability to maintain high-speed operation with low-resolution converters while accepting higher-resolution signals is crucial for effective performance. Therefore, understanding and addressing the reasons behind lower means compared to the control are essential for ensuring accurate statistical analyses, market stability, and efficient control system operation.