Population Variance Research Articles

Genome-wide association study and linkage mapping reveal TaqW-6B associated with water-extractable arabinoxylan content in wheat grain.

A novel QTL, TaqW-6B of water-extractable arabinoxylan content in the wheat grain on chromosome 6BL was identified and fine mapped in a narrow region 3.8Mb. Water-extractable arabinoxylan (WE-AX), an important component of hemicellulose, is associated with various abundant health benefits. In this study, QTLs for WE-AX content were detected in two populations: (1) a recombinant inbred line (RIL) population with 164 lines derived from a cross between Avocet and Chilero (AC population) genotyped with diversity array technology (DArT), and (2) a natural population of 243 varieties (CH population) genotyped with the Axiom wheat 660K single-nucleotide polymorphism (SNP) array. A stable QTL Qwe-ax.haust-6B, explaining 8.51-15.59% of the phenotypic variance, was mapped in the physical interval 459.38-572.09Mb on the long arm of chromosome 6B in the AC population, tightly linked with DArT markers 3,944,740 and 4,991,038 under three experimental conditions. The Qwe-ax.haust-6B was further narrowed down to be delimited in the physical interval 516.47-571.58Mb on chromosome 6BL, explaining 5.86-16.27% of the phenotypic variance in the CH population. Furthermore, we developed high-throughput kompetitive allele-specific PCR (KASP) markers to reconstruct the genetic linkage map in the AC population, and Qwe-ax.haust-6B was fine mapped into a narrow region named TaqW-6B, which was compressed between KASP-6B-3 and KASP-6B-6 at a physical distance of 3.8Mb. In the meanwhile, the markers were also validated in a natural population of 160 wheat lines (NP population). Consequently, this study is of great importance to provide the theoretical basis for cloning the key gene and developing functional markers for molecular breeding.

Polygenic and socioeconomic contributions to nicotine use and cardiometabolic health in early mid-life.

Early mid-life is marked by accumulating risks for cardiometabolic illness linked to health-risk behaviors like nicotine use. Identifying polygenic indices (PGI) has enriched scientific understanding of the cumulative genetic contributions to behavioral and cardiometabolic health, though few studies have assessed these associations alongside socioeconomic (SES) and lifestyle factors. Drawing on data from 2,337 individuals from the United States participating in the National Longitudinal Study of Adolescent to Adult Health, the current study assesses the fraction of variance in five related outcomes - use of conventional and electronic cigarettes, body mass index (BMI), waist circumference, and glycosylated hemoglobin (A1c) - explained by PGI, SES, and lifestyle. Regression models on African ancestry (AA) and European ancestry (EA) subsamples reveal that the fraction of variance explained by PGI ranges across outcomes. While adjusting for sex and age, PGI explained 3.5%, 2.2%, and 0% in the AA subsample of variability in BMI, waist circumference, and A1c, respectively (in the EA subsample these figures were 7.7%, 9.4%, and 1.3%). The proportion of variance explained by PGI in nicotine-use outcomes is also variable. Results further indicate that PGI and SES are generally complementary, accounting for more variance in the outcomes when modeled together versus separately. PGI are gaining attention in population health surveillance, but polygenic variability might not align clearly with health differences in populations or surpass SES as a fundamental cause of health disparities. We discuss future steps in integrating PGI and SES to refine population health prediction rules. Study findings point to the complementary relationship of polygenic indices (PGI) and socioeconomic indicators in explaining population variance in nicotine outcomes and cardiometabolic wellness. Population health surveillance and prediction rules would benefit from the combination of information from both polygenic and socioeconomic risks. Additionally, the risk for electronic cigarette use among users of conventional cigarettes may have a genetic component tied to the cumulative genetic propensity for heavy smoking. Further research on PGI for vaping is needed.

Predictive analysis of stochastic stock pattern utilizing fractional order dynamics and heteroscedastic with a radial neural network framework

Modelling of high-dimension chaotic, noisy, and non-stationary time series of complex fractal dynamics is a big challenge. In this research work, a novel approach of Leverage Convolution LC ARFIMA– GARCH model is presented for sequential learning of irregular, fractal dynamic patterns of stochastic time series dynamics. Different classical methods including the auto-regressive approach indecently are unable to capture fragile patterns and consequently convert some weak signals into random errors by creating white noise patterns. The Convolution Leverage provides an additional degree of freedom for critical noisy points and asymmetrical distribution dynamics to regulate the frequency of imbalance and skewed observation. The designed transformation in the ARFIMA model preserves the loss of information by shifting the skewed data distribution toward a normal pattern. The pattern of population parameters in the proposed leverage paradigm provides out of box approach to track and extract additional information on population variance patterns in the form of an additional degree of freedom to stabilize imbalance signals. The model can provide reliable performance for long-range dependence, particularly for mean reversion chaotic phase variation at critical non-differentiable points. The performance of the dynamic model is verified on chaotic real data of the Ireland Stock Market (ISEQ). The result statistics confirmed the optimized outcome with the addition of the GARCH heteroscedastic multimodal and radial basis neural network. The novel technique can help to address inherited challenges in the imbalance learning and extreme events of the time series modeling by monitoring the chaotic trajectory of fractal physical phenomena, particularly in, finance healthcare, climate, and intelligent sustainability.

Stochastic approach to a delayed in-host model of DENV transmission

Motivation. The immune response to the viral transmission experiences delays due to diverse biological factors and is affected by several factors. These include infection rate, rate of apoptosis and impact of the antibody-virus complex which exhibit unpredictable nature in a random environment. Objective. The main goal is to examine the impact of noise terms, introduced into every state variable, on a delayed in-host model of viral transmission. Methodology. To determine the intensity fluctuations and variances of all concerned state variables in the stochastic delayed model, which includes a constant delay and Gaussian white noise, the Fourier transformation method is employed. Results and Conclusions. The limiting value of the delay parameter is derived explicitly as well as numerically. The system experiences Hopf bifurcation, whenever the delay parameter crosses the limiting value which is shown graphically. The intensities and variances of different cells and virus populations are derived and the values are computed numerically. As the delay increases, the level of viremia decreases while other parameters maintain fixed values. The outcomes of data sensitivity, shown in graphical form, indicate that the transmission rate and supply rate of viruses are highly sensitive. Our findings suggest that the presence of noise causes fluctuations in the delayed model, leading to a noticeable impact on the transmission of the virus.

Double Exponential Ratio Estimator of a Finite Population Variance under Extreme Values in Simple Random Sampling

This article presents an improved class of efficient estimators aimed at estimating the finite population variance of the study variable. These estimators are especially useful when we have information about the minimum/maximum values of the auxiliary variable within a framework of simple random sampling. The characteristics of the proposed class of estimators, including bias and mean squared error (MSE) under simple random sampling are derived through a first-order approximation. To assess the performance and validate the theoretical outcomes, we conduct a simulation study. Results indicate that the proposed class of estimators has lower MSEs as compared to other existing estimators across all simulation scenarios. Three datasets are used in the application section to emphasize the effectiveness of the proposed class of estimators over conventional unbiased variance estimators, ratio and regression estimators, and other existing estimators.

Enhanced Estimation of Population Variance under Simple Random Sampling with an Application to Real Data

In this paper, we present an enhanced class of estimators for finite population variance under simple random sampling that incorporates an auxiliary information. The equations for bias and mean square error of the proposed estimators are produced up to the first order of approximation. Some well-known estimators are found as a special case of the proposed estimators for suitably chosen values of the scalars. The effectiveness of the proposed estimators is calculated in relation to the existing estimators both theoretically and empirically using five real data sets. The empirical results exhibit the superiority of the suggested estimators over to the existing estimators in each population.. KEYWORDS :Auxiliary variable, Mean square error, Variance estimation, Class of estimators.

Precision enhancement in variance estimation for complex environmental populations using adaptive cluster sampling

Estimating dispersion in populations that are extremely rare, hidden, geographically clustered, and hard to access is a well-known challenge. Conventional sampling approaches tend to overestimate the variance, even though it should be genuinely reduced. In this environment, adaptive cluster sampling is considered to be the most efficient sampling technique as it provides generally a lower variance than the other conventional probability sampling designs for the assessment of rare and geographically gathered population parameters like mean, total, variance, etc. The use of auxiliary data is very common to obtain the precise estimates of the estimators by taking advantage of the correlation between the survey variable and the auxiliary data. In this article, we introduced a generalized estimator for estimating the variance of populations that are rare, hidden, geographically clustered and hard-to-reached. The proposed estimator leverages both actual and transformed auxiliary data through adaptive cluster sampling. The expressions of approximate bias and mean square error of the proposed estimator are derived up to the first-order approximation using Taylor expansion. Some special cases are also obtained using the known parameters associated with the auxiliary variable. The proposed class of estimators is compared with available estimators using simulation and real data applications.

Efficient Classes of Robust Ratio Type Estimators of Mean and Variance in Adaptive Cluster Sampling

This paper proposes two classes of robust ratio type estimators of finite population mean and two classes of robust ratio type estimators of finite population variance using a single auxiliary variable under the adaptive cluster sampling design. Seven robust ratio type estimators have been developed from each class. The generalized expressions of bias and mean square error for each class have been derived up to the first order of approximation. The exact expressions of bias and MSE for all the developed estimators have been presented. Using simulation studies conducted in R programming, the high efficiency of all the developed estimators over similar existing estimators in adaptive cluster sampling has been demonstrated. . KEYWORDS :Adaptive cluster sampling, Classes of robust ratio type estimators, Hidden clustered data, Estimation of mean, Estimation of variance.

Determination of Household Food Consumption Patterns in East Baturaja District OKU Regency

The income level will influence household spending patterns. In conditions of limited income, low-income or poor households will prioritise allocating their income to buying food. This income will be used to spend on food for survival so it will affect the quantity and quality of household food consumption. Good quality food consumption indicates that nutritional adequacy levels are met. This research analyses the factors influencing household food consumption patterns in East Baturaja District, OKU Regency. This research used a survey method and was carried out in three sub-districts in East Baturaja District, OKU Regency, namely Kemala Raja, Sekar Jaya and Tanjung Baru Villages. The method for determining respondents is random sampling using the Parell et all (1973) formula with the statement if the variance of the total population is unknown, so the best way to determine the sample is to use a percentage of the entire population, namely 2%, 5%, 10%, 20% or 50% of the population. The population is 2126 families, obtained by 2%, namely 42.52 and determined to be 43 families. This research used multiple linear regression models to determine the factors influencing food consumption patterns using multiple linear analyses. Based on the research results, it can be concluded that the factors that influence household food consumption patterns in East Baturaja District, OKU Regency, are income (X1), number of family members (X2), age (X3), and education level (X4). The variable that has the most influence on household food consumption patterns in East Baturaja District, OKU Regency, is the income level variable (X1). Simultaneously or together, the income variable (X1), number of family members (X2), age (X3), and education level (X4) has a significant influence. R2 showed this with a value of 97.9%.

Low-density lipoprotein cholesterol response to statins according to comorbidities and co-medications: A population-based study

The response of low-density lipoprotein cholesterol (LDL-C) to statin therapy is variable, and may be affected by the presence of co-morbid conditions or the use of concomitant medications. Systematic variation in the response to statins based on these factors could affect the selection of the statin treatment regimen in population subgroups. We investigated whether common comorbidities and co-medications had clinically important effects on statin responses in individual patients. This register-based cohort study included 89,006 simvastatin or atorvastatin initiators with measurements of pre-statin and on-statin LDL-C levels, in Denmark, 2008-2018. We defined statin response as the percentage reduction in LDL-C, and used linear regression to estimate percentage reduction differences (PRD) according to 175 chronic comorbidities and 99 co-medications. We evaluated both the statistical significance (P-values corrected for multiple testing) and the clinical importance (PRD of 5 percentage points or more) of the observed associations. Concomitant use of oral blood-glucose lowering drugs, which included metformin in 96% of treated individuals, was associated with a greater response to statin therapy that was both statistically significant and clinically important, with a PRD of 5.18 (95% confidence interval: 4.79 to 5.57). No other comorbidity or co-medication reached the prespecified thresholds for a significant, clinically important effect on statin response. Overall, comorbidities and co-medications had little effect on statin response, and altogether explained only 1.7% of the total observed population variance. Most of the studied comorbidities and co-medications did not have a clinically important effect on statin response, suggesting no need to modify treatment regimens. However, use of metformin was associated with a significantly enhanced LDL-C response to statins, suggesting that lower statin doses may be effective in patients taking metformin.

An Estimator of Population Variance Using Multi-Auxiliary Information

In this article, an estimator of population variance using multi-auxiliary information has been proposed. It is seen that under certain conditions, the proposed estimator is less biased and more efficiency than existing estimators. Theoretical results are supported numerically. Moreover, a simulation study also has been made.

Hybrid class of robust type estimators for variance estimation using mean and variance of auxiliary variable

This study is completed for the estimation of unknown population variance for the variable of mean and variance of interest. To accomplish this task, a new generalized class of robust kind of variance estimators proposed utilizing known descriptives of auxiliary variable, for example, Mid-range, Hodges-Lehmann Mean, Tri-mean, deciles mean, coefficient of skewness, interquartile range, first quartile, coefficient of kurtosis, semi-interquartile average, inter decile range and Mean, etc. These conventional measures of auxiliary variable improve the accuracy of the suggested class under simple random sampling without replacement (SRSWOR) scheme. The properties such as the bias, mean square errors (MSE), and least MSE of the suggested class are derived up to first order of approximation. The superiority conditions of the developed class of estimators over existing estimators are also made out theoretically. Finally, numerical representation is also completed for the motivations behind the article. The usual variance estimator is considered as a benchmark for comparing all considered estimators in numerical illustration. The results have been indicated that the suggested class is performing better than the usual variance estimator and all other thoughts about existing estimators.

Using Extant Data to Improve Estimation of the Standardized Mean Difference

This article presents methods for using extant data to improve the properties of estimators of the standardized mean difference (SMD) effect size. Because samples recruited into education research studies are often more homogeneous than the populations of policy interest, the variation in educational outcomes can be smaller in these samples than is reflective of the true variation in the population. This affects effect size estimation since the sample standard deviation is used in the denominator of the SMD. We propose leveraging extant data on sample variance estimates from multiple studies, made available via clearinghouse databases such as the What Works Clearinghouse, to standardize a mean difference. This allows effect sizes to be benchmarked across a common and broad population, thus enabling better comparability across studies and interventions. We derive the new estimators of the population variance and the corresponding SMD, which pool sample variances from multiple studies using both an analysis of variance and a meta-analytic framework. We demonstrate the properties of these estimators via analytic and simulation results and offer recommendations for when these estimators are appropriate in practice.

Improvement in variance estimation using transformed auxiliary variable under simple random sampling

This paper offers a novel approach to formulate efficient ratio estimator of the population variance using a transformed auxiliary variable. The impact of transformation on auxiliary information has also been discussed. It is observed that incorporating a transformed auxiliary variable result in a high gain in efficiency. Theoretical properties of the newly developed estimators have been derived. The empirical and simulation studies show that the suggested estimators outperformed the existing estimators.

Shrinkage Testimator for the Common Mean of Several Univariate Normal Populations

The challenge of combining two unbiased estimators is a common occurrence in applied statistics, with significant implications across diverse fields such as manufacturing quality control, medical research, and the social sciences. Despite the widespread relevance of estimating the common population mean μ, this task is not without its challenges. A particularly intricate issue arises when the variations within populations are unknown or possibly unequal. Conventional approaches, like the two-sample t-test, fall short in addressing this problem as they assume equal variances among the two populations. When there exists prior information regarding population variances (σi2,i=1,2), with the consideration that σ12 and σ22 might be equal, a hypothesis test can be conducted: H0:σ12=σ22 versus H1:σ12≠σ22. The initial sample is utilized to test H0, and if we fail to reject H0, we gain confidence in incorporating our prior knowledge (after testing) to estimate the common mean μ. However, if H0 is rejected, indicating unequal population variances, the prior knowledge is discarded. In such cases, a second sample is obtained to compensate for the loss of prior knowledge. The estimation of the common mean μ is then carried out using either the Graybill–Deal estimator (GDE) or the maximum likelihood estimator (MLE). A noteworthy discovery is that the proposed preliminary testimators, denoted as μ^PT1 and μ^PT2, exhibit superior performance compared to the widely used unbiased estimators (GDE and MLE).

Constructing group sequential tests with data monitoring

Objective:Group sequential tests are procedures for repeatedly analysing accruing data, and aim to reduce test time by stopping data collection as soon as an effect is deemed statistically present or absent. Data monitoring, on the other hand, aims to control statistical power by repeatedly re-estimating sample variance from the accruing data, and then adjusting the sample size accordingly. The current work presents a numerical framework for combining group sequential tests with data monitoring, thus leveraging the best of both methods. Methods:The approach revolves around numerically convolving truncated probability density functions, which leads to numerically tracktable distributions for the sequential test statistic. This then allows conventional power analysis methodologies to be used during the data monitoring procedure, thus keeping the procedure relatively intuitive and transparent. The test operating characteristics of the procedure were evaluated extensively in simulations and in Auditory Brainstem Response data. Results:A good control over statistical power was observed, which was attributed to data monitoring. In some test conditions, however, data monitoring led to inflated type-I error rates and a reduced control over statistical power. This was attributed to sample variance estimation errors, and was largely overcome by initiating data monitoring after at least ∼40-90 samples had accrued and/or by leaving data blinded. Conclusion/Significance:When there is uncertainty regarding the effect size and/or the population variance, then group sequential tests with data monitoring may offer a solution, providing an efficient means to reach an unambiguous test outcome in terms of effect present/absent.

Neutrosophic robust ratio type estimator for estimating finite population mean

Various authors have put their sincere efforts into proposing ratio estimators for estimating the population's mean and variance under different situations and sampling methods. But the problem arises when data is unstable, imprecise, ambiguous, incomplete and vague. In such situations, classical methods of estimation do not yield precise results, as these methods are not meant for such problems. Given this difficulty, Neutrosophic statistics are the only alternative as it deals with indeterminacy. So in this study, we have proposed a generalized Neutrosophic robust ratio type estimator which can be used to provide good results in such situations, as well as in the case of the presence of outliers. For the evaluation point of view, we have made use of real data set and simulation study to check the efficacy of our suggested estimators over the mentioned existed estimators.

Physical activity among pregnant inpatients and outpatients and associations with anxiety

ObjectivePhysical activity is linked to lower anxiety, but little is known about the association during pregnancy. This is especially important for antepartum inpatients, who are known to have increased anxiety yet may not be able to achieve target levels of physical activity during hospitalization. We compared physical activity metrics between pregnant inpatients and outpatients and explored correlations with anxiety. Materials and MethodsThis was a prospective cohort between 2021 and 2022 of pregnant people aged 18–55 years carrying singleton gestations ≥ 16 weeks. Three exposure groups were matched for gestational age: 1) outpatients from general obstetric clinics; 2) outpatients from high-risk Maternal-Fetal Medicine obstetric clinics; and 3) antepartum inpatients. Participants wore Actigraph GT9X Link accelerometer watches for up to 7 days to measure physical activity. The primary outcome was mean daily step count. Secondary outcomes were metabolic equivalent tasks (METs), hourly kilocalories (kcals), moderate to vigorous physical activity (MVPA) bursts, and anxiety (State-Trait Anxiety Inventory [STAI]). Step counts were compared using multivariable generalized estimating equations adjusting for maternal age, body-mass index, and insurance type as a socioeconomic construct, accounting for within-group clustering by gestational age. Spearman correlations were used to correlate anxiety scores with step counts. Results58 participants were analyzed. Compared to outpatients, inpatients had significantly lower mean daily steps (primary outcome, adjusted beta −2185, 95 % confidence interval [CI] −3146, −1224, p < 0.01), METs (adjusted beta −0.18, 95 % CI −0.23, −0.13, p < 0.01), MVPAs (adjusted beta −38.2, 95 % CI −52.3, −24.1, p < 0.01), and kcals (adjusted beta –222.9, 95 % CI −438.0, −7.8, p = 0.04). Over the course of the week, steps progressively decreased for inpatients (p-interaction 0.01) but not for either of the outpatient groups. Among the entire cohort, lower step counts correlated with higher anxiety scores (r = 0.30, p = 0.02). ConclusionWe present antenatal population norms and variance for step counts, metabolic equivalent tasks, moderate to vigorous physical activity bursts, and kcals, as well as correlations with anxiety. Antepartum inpatients had significantly lower physical activity than outpatients, and lower step counts correlated with higher anxiety levels. These results highlight the need for physical activity interventions, particularly for hospitalized pregnant people.

Validity and reproducibility of the PERSIAN Cohort food frequency questionnaire: assessment of major dietary patterns

BackgroundDietary patterns, encompassing an overall view of individuals’ dietary intake, are suggested as a suitable means of assessing nutrition’s role in chronic disease development. The aim of this study was to evaluate the validity and reproducibility of a food frequency questionnaire (FFQ) designed for use in the Prospective Epidemiological Research Studies in IrAN (PERSIAN), by comparing major dietary patterns assessed by the FFQ with a reference method.MethodsStudy participants included men and women who enrolled in the PERSIAN Cohort Study at seven of the eighteen centers. These centers were chosen to include dietary variations observed among the different Iranian ethnic populations. Two FFQ were completed for each participant over a one-year study period (FFQ1 upon enrollment and FFQ2 at the end of the study), with 24 interviewer-administered 24-hour dietary recalls (24 h) being completed monthly in between. Spearman correlation coefficients (SCC) were used comparing FFQs 1 and 2 to the 24 h to assess validity, while FFQ1 was compared to FFQ2 to assess reproducibility of the questionnaire.ResultsThree major dietary patterns—Healthy, Low Protein/High Carb and Unhealthy—were identified, accounting for 70% of variance in the study population. Corrected SCC ranged from 0.31 to 0.61 in the validity and from 0.34 to 0.57 in reproducibility analyses, with the first two patterns, which accounted for over 50% of population variance, correlated at above 0.5 in both parameters, showing acceptable findings.ConclusionsThe PERSIAN Cohort FFQ is suitable for identification of major dietary patterns in the populations it is used for, in order to assess diet-disease relationships.

Estimation of heterogeneous population variance using memory-type estimators based on EWMA statistic in the presence of measurement error for time-scaled surveys

In this present article, we have suggested memory-type ratio, exponential ratio, product and exponential product estimators based on exponentially weighted moving average statistic for the estimation of heterogeneous population variance using stratified sampling design in presence of measurement error for time-scaled surveys. Mathematical expressions of approximate mean square error are derived using Taylor and exponential expansions for the proposed memory-type estimators. We have also discussed the situations in which the memory-type estimators would perform efficiently than the conventional estimators. The results of extensive simulation study revealed that the proposed memory-type estimators based exponentially weighted moving average statistic would perform better than the conventional estimators in the presence measurement error for time-scaled surveys under certain condition.