Showing papers on "Population proportion published in 2021"

The Spatiotemporal Interaction Effect of COVID-19 Transmission in the United States

Abstract: (1) Background: Human mobility between geographic units is an important way in which COVID-19 is spread across regions. Due to the pressure of epidemic control and economic recovery, states in the United States have adopted different policies for mobility limitations. Assessing the impact of these policies on the spatiotemporal interaction of COVID-19 transmission among counties in each state is critical to formulating epidemic policies. (2) Methods: We utilized Moran’s I index and K-means clustering to investigate the time-varying spatial autocorrelation effect of 49 states (excluding the District of Colombia) with daily new cases at the county level from 22 January 2020 to 20 August 2020. Based on the dynamic spatial lag model (SLM) and the SIR model with unreported infection rate (SIRu), the integrated SLM-SIRu model was constructed to estimate the inter-county spatiotemporal interaction coefficient of daily new cases in each state, which was further explored by Pearson correlation test and stepwise OLS regression with socioeconomic factors. (3) Results: The K-means clustering divided the time-varying spatial autocorrelation curves of the 49 states into four types: continuous increasing, fluctuating increasing, weak positive, and weak negative. The Pearson correlation analysis showed that the spatiotemporal interaction coefficients in each state estimated by SLM-SIRu were significantly positively correlated with the variables of median age, population density, and proportions of international immigrants and highly educated population, but negatively correlated with the birth rate. Further stepwise OLS regression retained only three positive correlated variables: poverty rate, population density, and highly educated population proportion. (4) Conclusions: This result suggests that various state policies in the U.S. have imposed different impacts on COVID-19 transmission among counties. All states should provide more protection and support for the low-income population; high-density populated states need to strengthen regional mobility restrictions; and the highly educated population should reduce unnecessary regional movement and strengthen self-protection.

Robust confidence intervals for a proportion using ranked-set sampling

Abstract: We develop two new approximate confidence interval methods for estimating a population proportion using balanced ranked-set sampling (RSS). Unlike existing RSS-based methods, the new methods control the coverage probability well not just under perfect rankings, but also under imperfect rankings. One method uses a Wilson-type interval, and the other is based on making a mid-P adjustment to a Clopper–Pearson-type interval. Both methods rely on a new maximum-likelihood-based method for estimating the proportions in the judgment strata when the overall proportion is given, and both can be computed even for large sample sizes.

COVID-19-related state-wise racial and ethnic disparities across the USA: an observational study based on publicly available data from The COVID Tracking Project.

Abstract: Objective To evaluate COVID-19 infection and mortality disparities in ethnic and racial subgroups in a state-wise manner across the USA. Methods Publicly available data from The COVID Tracking Project at The Atlantic were accessed between 9 September 2020 and 14 September 2020. For each state and the District of Columbia, % infection, % death, and % population proportion for subgroups of race (African American/black (AA/black), Asian, American Indian or Alaska Native (AI/AN), and white) and ethnicity (Hispanic/Latino, non-Hispanic) were recorded. Crude and normalised disparity estimates were generated for COVID-19 infection (CDI and NDI) and mortality (CDM and NDM), computed as absolute and relative difference between % infection or % mortality and % population proportion per state. Choropleth map display was created as thematic representation proportionate to CDI, NDI, CDM and NDM. Results The Hispanic population had a median of 158% higher COVID-19 infection relative to their % population proportion (median 158%, IQR 100%–200%). This was followed by AA, with 50% higher COVID-19 infection relative to their % population proportion (median 50%, IQR 25%–100%). The AA population had the most disproportionate mortality, with a median of 46% higher mortality than the % population proportion (median 46%, IQR 18%–66%). Disproportionate impact of COVID-19 was also seen in AI/AN and Asian populations, with 100% excess infections than the % population proportion seen in nine states for AI/AN and seven states for Asian populations. There was no disproportionate impact in the white population in any state. Conclusions There are racial/ethnic disparities in COVID-19 infection/mortality, with distinct state-wise patterns across the USA based on racial/ethnic composition. There were missing and inconsistently reported racial/ethnic data in many states. This underscores the need for standardised reporting, attention to specific regional patterns, adequate resource allocation and addressing the underlying social determinants of health adversely affecting chronically marginalised groups.

Estimation of parameters of logistic regression for two-stage randomized response technique

Abstract: When a survey study is related to sensitive issues such as political orientation, sexual orientation, and income, respondents may not be willing to reply truthfully, which leads to bias results. To protect the respondents’ privacy and improve their willingness to provide true answers, Warner (J Am Stat Assoc 60:63–69, 1965) proposed the randomized response (RR) technique in which respondents select a question by means of a random device in order to ensure that they maintain privacy. Huang (Stat Neerl 58:75–82, 2004) extended the RR design of Warner (1965) to propose a two-stage RR design. Not only can this method be used to estimate the population proportion of persons with a sensitive characteristic, but also estimate the honest answer rate in the first stage. This work develops a covariate extension of the two-stage RR design of Huang (2004) by applying logistic regression to investigate the effects of covariates on a sensitive characteristic and an honest response. Simulation experiments are conducted to study the finite-sample performance of the maximum likelihood estimators of the logistic regression parameters. The proposed methodology is applied to analyze the survey data of sexuality of freshmen at Feng Chia University in Taiwan in 2016.

Estimation of population proportion using concomitant based ranked set sampling

Abstract: This paper considers the concomitant based double ranked set sampling (CDRSS) for estimating the population proportion and compares with existing concomitant based ranked set sampling (CRSS) and si...

Estimation of finite population distribution function of sensitive variable

Abstract: The finite population proportion of a sensitive characteristic is estimated indirectly by using Randomized Response (RR) Techniques (RRT’s) pioneered by Warner (1965) followed by several other RRT’...

A Bayesian Approach to Linking a Survey and a Census via Small Areas

Abstract: We predict the finite population proportion of a small area when individual-level data are available from a survey and more extensive household-level (not individual-level) data (covariates but not responses) are available from a census. The census and the survey consist of the same strata and primary sampling units (PSU, or wards) that are matched, but the households are not matched. There are some common covariates at the household level in the survey and the census and these covariates are used to link the households within wards. There are also covariates at the ward level, and the wards are the same in the survey and the census. Using a two-stage procedure, we study the multinomial counts in the sampled households within the wards and a projection method to infer about the non-sampled wards. This is accommodated by a multinomial-Dirichlet–Dirichlet model, a three-stage hierarchical Bayesian model for multinomial counts, as it is necessary to account for heterogeneity among the households. The key theoretical contribution of this paper is to develop a computational algorithm to sample the joint posterior density of the multinomial-Dirichlet–Dirichlet model. Specifically, we obtain samples from the distributions of the proportions for each multinomial cell. The second key contribution is to use two projection procedures (parametric based on the nested error regression model and non-parametric based on iterative re-weighted least squares), on these proportions to link the survey to the census, thereby providing a copy of the census counts. We compare the multinomial-Dirichlet–Dirichlet (heterogeneous) model and the multinomial-Dirichlet (homogeneous) model without household effects via these two projection methods. An example of the second Nepal Living Standards Survey is presented.

A randomized response model based on blank card and geometric distribution to estimate sensitive proportion

Abstract: Using geometric probability (GP) distribution as a randomization device, a new randomized response (RR) model has been proposed which has an immense potential to estimate the human population proportion that possess a stigmatized character. Privacy protection measure of proposed model and some of its properties have been investigated. In addition, empirical experiments are conducted to validate the theoretical results, which demonstrate the better performance of the suggested estimators over their direct competitors. Finally, results are analyzed and appropriate suggestions are made available to survey practitioners when dealing with sensitive aspects.

On estimation of population proportion in diagonal systematic sampling

Abstract: Systematic sampling is a popular probability sampling scheme for obtaining a sample from a population having a finite size. The diagonal systematic sampling design is a type of systematic sampling scheme which has been studied by many researchers during the last 2 decades. The existing literature on the study of diagonal systematic sampling and its various forms is limited to quantitative characteristics only. This paper aims to study the estimation of finite population proportion of units which possess a particular qualitative characteristic under diagonal systematic sampling scheme. It is found that for small sample sizes, the diagonal systematic sampling scheme provides more efficient estimates of finite population proportion as compared to simple random sampling and linear systematic sampling.

RSS in Other Parameteric and Non-parametric Inference

Abstract: In Chap. 19, the method of RSS for estimating the population mean of the distribution has been discussed. The theory of RSS has been extended in this chapter to estimate location and scale parameters of distributions, population proportion, and quantiles. The original concept (McIntyre 1952) of RSS is completely non-parametric in nature and assumed that the population distribution is not known beforehand, therefore, the concept of RSS to estimate location, scale, and quantiles of the distributions is helpful. The application of RSS has also been attempted for the non-parametric inference. Some non-parametric tests based on one sample and two samples are discussed.

Maximum likelihood estimation of two-sample population proportions under constraint on their difference

Abstract: We derive the maximum likelihood estimate (MLE) of a population proportion when it differs from the same of a second population by a known value. This constrained MLE (CMLE) has a closed form in li...

Mathematical properties and finite-population correction for the Wilson score interval

Abstract: In this paper we examine the properties of the Wilson score interval, used for inferences for an unknown binomial proportion parameter. We examine monotonicity and consistency properties of the interval and we generalise it to give two alternative forms for inferences undertaken in a finite population. We discuss the nature of the "finite population correction" in these generalised intervals and examine their monotonicity and consistency properties. This analysis gives the appropriate confidence interval for an unknown population proportion or unknown unsampled proportion in a finite or infinite population. We implement the generalised confidence interval forms in a user-friendly function in R.