Population Mean Research Articles

Mapping the Growth of Supermassive Black Holes as a Function of Galaxy Stellar Mass and Redshift

The growth of supermassive black holes is strongly linked to their galaxies. It has been shown that the population mean black hole accretion rate ( BHAR¯ ) primarily correlates with the galaxy stellar mass (M ⋆) and redshift for the general galaxy population. This work aims to provide the best measurements of BHAR¯ as a function of M ⋆ and redshift over ranges of 109.5 < M ⋆ < 1012 M ⊙ and z < 4. We compile an unprecedentedly large sample with 8000 active galactic nuclei (AGNs) and 1.3 million normal galaxies from nine high-quality survey fields following a wedding cake design. We further develop a semiparametric Bayesian method that can reasonably estimate BHAR¯ and the corresponding uncertainties, even for sparsely populated regions in the parameter space. BHAR¯ is constrained by X-ray surveys sampling the AGN accretion power and UV-to-infrared multiwavelength surveys sampling the galaxy population. Our results can independently predict the X-ray luminosity function (XLF) from the galaxy stellar mass function (SMF), and the prediction is consistent with the observed XLF. We also try adding external constraints from the observed SMF and XLF. We further measure BHAR¯ for star-forming and quiescent galaxies and show that star-forming BHAR¯ is generally larger than or at least comparable to the quiescent BHAR¯ .

Associations between Multiple Health Indicators and Carotid Artery Intima-Media Thickness in A Healthy and Active Elderly Population.

The purpose of this study was to examine correlations between health indicators (age, BMI, blood pressure (BP), functional strength (FS), handgrip strength, and predicted VO2 max) and carotid intima-media thickness (cIMT) in an active 50 years+ population. Study participants' mean cIMT was also compared to the cIMT mean of the general population. Health screenings were conducted on 1818 participants at the Huntsman World Senior Games from 2016 to 2019. Pearson's correlations, Spearman's correlations, and ANOVA were performed using SPSS. Weak but significant correlations were evident between cIMT and age (r = 0.283, p < 0.001), systolic BP (r = 0.253, p = 0.001), diastolic BP (r = 0.074, p = 0.016), weight (r = 0.170, p < 0.001), height (r = 0.153, p < 0.001), handgrip L (r = 0.132, p < 0.001), handgrip R (r = 0.074, p < 0.029), and BMI (r = 0.07, p = 0.029); non-significant correlations were evident with predicted VO2 max (r = -0.035, p = 0.382), and FS (r = -0.025, p = 0.597). When controlling for age, systolic BP, and sex, only handgrip L (r = 0.225, p = 0.014) was significantly correlated with cIMT. Mean cIMT for this cohort was lower across all sexes and age-matched groups (cIMT = 0.6967 mm (±0.129)). Physical activity is linked to reduced cIMT. Most health-related indicators in this study were significantly but weakly correlated with cIMT. Additional research is needed before common indicators can be used as a surrogate for cIMT and CVD risk. Results from this study can provide clinicians with additional information to reduce CVD risk through modifiable risk factors. Classic CVD risk factors such as systolic BP and BMI should be considered in patients regardless of lifestyle.

Indirect Questioning Technique Related to Sensitive Quantitative Variables with Options for Direct, Randomized and Item Count Responses

Randomized Response (RR) Technique (RRT) pioneered by Warner (1965) is a useful tool to elicit responses on sensitive characteristics, such as induced abortions, drug abuse, drunken driving, total amount of counterfeit notes of a particular denomination held by individuals in the population, etc. There exists a huge literature on Randomized Response (RR) devices for estimation of finite population mean of quantitative variables, sensitive in nature mostly based on Eichhorn and Hayre (1983). Device-I and Device-II vide Chaudhuri and Christofides (2013) allow estimation of population mean of sensitive quantitative variables using sample chosen by a general sampling design. On the other hand, Item Count Technique (ICT), described elaborately in Chaudhuri and Christofides (2013), is an alternative to RRT for respondents who do not choose to provide RRs. While some respondents may find a variable as sensitive, others may find it innocuous enough to provide a direct response (DR) about his/her true value. In such a case, Optional Randomized Response (ORR) Technique (ORRT) with options for DR and RR was introduced by Chaudhuri and Mukherjee (1985). Pal (2007) proposed an ORR device which offers choices for RR and ICT to the respondents for giving their answers. A new ORRT with options for DR, RR and ICT was provided by Shaw and Pal (2021) for eliciting indirect responses on sensitive characteristics. As this device relates to estimation of population proportion of sensitive characteristics, an attempt has been made to extend it for sensitive quantitative variables. Further, to take care of individuals’ varying choices for DR, RR and ICT and to protect the privacy of the respondents’ choices, this paper develops an ORR device allowing the respondents chosen by a general sampling design, to choose any one of the three options according to their choices. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 155-170

Ratio Type Estimator for Balanced Sampling Plan excluding Adjacent Units

The study variables are usually correlated with the auxiliary variables and therefore their correlation could easily be computed. In this paper, the correlation coefficient is used for the estimation procedure, and therefore, we proposed a Horvitz-Thompson ratio-type estimator using correlation coefficient for Balanced Sampling plan excluding Adjacent units (BSA plan). It has been illustrated theoretically and empirically that the proposed Horvitz-Thompson ratio-type estimator is more precise than the Horvitz-Thompson estimator based on BSA plan. The proposed estimator provides an opportunity to utilise the auxiliary information for the estimation of population mean for BSA plan and useful for many real-life experiments. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 103-114

Efficient generalized estimators of population mean in the presence of non-response and measurement error

Researchers frequently encounter significant challenges in sampling surveys, particularly related to non-response and measurement errors. These challenges pose substantial obstacles that can undermine the precision and dependability of survey outcomes. In this study, we introduced population mean estimators that explicitly address non-response and measurement error, employing both single-phase and two-phase sampling techniques. We derived the bias and mean square error expressions of the proposed estimators up to the first order of approximation. To evaluate the efficiency of the proposed estimators tS1 and tS2, the percent relative efficiencies have been computed. The percent relative efficiency results of population 1 in single-phase sampling for the proposed estimator are 612.258, 610.837, and 609.061, while for two-phase sampling, the results are 576.823, 581.090, and 586.541. These results show the superior performance of the proposed estimators when compared to tSP, tMA1, tMS and tTB, as reflected in Tables 4 and 5.

Benchmarking lean construction conformance in Pakistan’s construction industry

PurposeLean construction (LC) is an innovative approach in the construction industry that has shown significant success in developed countries. Although LC has potential in the construction sector of Pakistan, it has not been extensively explored. This study aims to address this knowledge gap by identifying and predicting current lean practices and assessing the strengths and weaknesses of LC implementation in Pakistan.Design/methodology/approachUsing robust statistical methods to analyze 92 valid responses, the study reveals that approximately 54% of lean practices are currently in use in the construction industry of Pakistan, with a population mean ranging from 52.7% to 55.6%.FindingsSurprisingly, the research identifies instances where some construction firms in Pakistan are implementing LC practices, even though they have only a limited understanding of its underlying principles. Notably, certain subprinciples, such as visual management, top management commitment to change, employee training, process cycle time reduction and production optimization, are less integrated within the construction industry. Exploring the possibility of implementing LC, recommendations for strategies to implement LC in Pakistan are suggested, aligning with the conceptual model proposed by the researchers.Originality/valueThe novelty of this work offers insights that can serve as a comprehensive guide for developing nations. It provides a structured approach to assess and benchmark LC practices, which, in turn, can contribute to a more efficient and effective construction industry. Moreover, the strategies proposed in this research can aid developing countries in the efficient implementation of LC. This will have a positive implication for both economic and developmental outcomes.

SEASONAL INCIDENCE OF Rhopalosiphum maidis (Fitch) ON MAIZE CROP IN GUNTUR AND KRISHNA DISTRICTS OF ANDHRA PRADESH

The seasonal occurrence of Rhopalosiphum maidis (Fitch) on maize was carried out in the Guntur and Krishna districts of Andhra Pradesh during rabi, 2021-2022 and rabi, 2022-2023. A total of 30 farmer fields were selected from six mandals of maize growing areas viz., Bapatla, Tsunduru, Ponnur, Karlapalem in Guntur district and two mandals viz., Mopidevi and Avanigadda in Krishna district.The population of aphids was recorded at weekly intervals on randomly selected 25 plants of each farmer field from germination to harvesting stage of the crop. The aphids were counted as number of aphids on 1 cm2 leaf area from three leaves (top, middle, lower) of eachplant. The data was statistically analyzed using SPSS for Windows (version 20.0) and Microsoft Excel 2016. In rabi 2021-22 the initial incidence of aphids was commenced from SMW5 (0.21 aphids sq. cm-1) and the highest mean population was recorded in SMW 9 (3.64 aphids sq. cm-1). In rabi 2022-23 the initial incidence was first noticed during SMW4 (1.71 aphids sq. cm-1) and the peak incidence was noticed during SMW 6(4.96 aphids sq. cm-1).The population of aphids showed a positive significant correlation with maximum temperature in both the seasons i.e., rabi, 2021- 22 (r=0.685**) and rabi, 2022-23 (r=0.612*).

Selecting an appropriate statistical model for estimating genetic parameters: A case study of pig maternal breeds in Czechia

A three-trait statistical model was designed for backfat thickness (BFT) and loin eye muscle depth (LMD) with accompanying variable average daily gain (ADG). Data from 82,507 Czech Large White and 37,556 Czech Landrace pigs were collected during on-farm performance testing from 2013 to 2022. Several animal models were tested with different combinations of effects and complexity. Model performance was evaluated by the linear regression (LR) method and predictivity. In Czech Large White, the preferred model comprised fixed effects of sex, birth year, herd, and ultrasound device, as well as random effects of herd-year-season (HYS), litter, and animal. In Czech Landrace, HYS was treated as fixed instead. The inclusion of maternal effects was not supported due to questionable impact on the main statistics. The selected model yielded mean absolute bias of 0.11 and 0.19, mean determination of 0.39 and 0.21, mean population accuracy of 0.36 and 0.31, and mean predictivity of 0.13 and 0.08 in Large White and Landrace, respectively. Heritability estimates were overall lower than those reported by other authors: 0.23, 0.10, and 0.10 in Large White and 0.26, 0.10, and 0.09 in Landrace for ADG, BFT, and LMD, respectively. Genetic correlations, on the other hand, reached relatively high values: 0.61, 0.61, and 0.66 in Large White and 0.71, 0.92, and 0.70 in Landrace for ADG-BFT, ADG-LMD, and BFT-LMD, respectively.

Transformed Regression Type Estimators in the Presence of Missing Observations: Case Studies on COVID-19 Incidence in Chiang Mai, Thailand

The transformation technique can be used to modify the shape of the variable to improve the performance of the population mean estimator. In the presence of missing data, before estimating the population mean using standard statistical methods, missing data has to be taken care of. In this study, we focus on new transformed regression type estimators when missing data are present in the study variable under the uniform nonresponse mechanism and assume that the population mean of the auxiliary variable is unavailable which usually occurs in practice. An auxiliary variable can assist by increasing the efficacy of estimating the population mean. The bias and mean square error are investigated up to the first order degree approximation using the Taylor series. A simulation and case studies on COVID-19 incidence in Chiang Mai, Thailand are used to assess the performance of the new transformed estimators. The estimated number of COVID-19 patients who have pneumonia and require high-flow oxygen and the estimated daily confirmed cases of COVID-19 in Chiang Mai from the best proposed estimator are around 17 cases and 118 cases, respectively.

Compromise optimum allocation in neutrosophic multi-character survey under stratified random sampling using neutrosophic fuzzy programming

Survey sampling has wide range of applications in social and scientific investigation to draw inference about the unknown parameter of interest. In complex surveys, the sample information about the study variable cannot be expressed by a precise number under uncertain environment due fuzziness and indeterminacy. Therefore, this information is expressed by neutrosophic numbers rather than the classical numbers. The neutrosophic statistics, which is generalization of classical statistics, deals with the neutrosophic data that has some degree of indeterminacy and fuzziness. In this study, we investigate the compromise optimum allocation problem for estimating the population means of the neutrosophic study variables in a multi-character stratified random sampling under uncertain per unit measurement cost. We proposed the intuitionistic fuzzy cost function, modeling the fuzzy uncertainty in stratum per unit measurement cost. The compromise optimum allocation problem is formulated as a multi-objective intuitionistic fuzzy optimization problem. The solution methodology is suggested using neutrosophic fuzzy programming and intuitionistic fuzzy programming approaches. A numerical study includes the means estimation of atmospheric variables is presented to explore the real-life application, explain the mathematical formulation, and efficiency comparison with some existing methods. The results show that the suggested methods produce more precise estimates with less utilization of survey resources as compared to some existing methods. The Python is used for statistical analysis, graphical designing and numerical optimization problems are solved using GAMS.

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults

BackgroundPredicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults. ObjectivesTo generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y. MethodsTEE derived from “gold standard” methods from n = 1657 (n = 1019 females, age range 65–90 y), was used to generate PAL values. PAL ranged 1.28–2.05 for males and 1.26–2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations. ResultsEquations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = −5680.17 − 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = −5290.72 − 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%. ConclusionsThese new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.

Abstract P399: Cardiovascular Risk Factors and Measures of Early Atherosclerosis in Children With Down Syndrome

Background: Children with Down syndrome (DS) have unique cardiovascular (CV) risk profiles compared to the general population. Early markers of atherosclerosis can be measured and are associated with CV risk factors, although limited data are available for children with DS. Methods: A multicenter, cross-sectional study of children with DS between 8-18 years. CV risks assessed: BMI, blood pressure, diet, activity, smoke exposure, congenital heart disease, hypothyroid, cancer, apnea, family history of early coronary disease, lipids, lipoprotein subfractions, glucose, insulin, and HS-CRP. Vascular testing included pulse wave velocity (PWV) and carotid intimal medial thickness (CIMT). Prevalence of categorical CV risks were compared to population prevalence from NHANES data using Fisher’s exact or chi squared. Vascular measures were converted to Z scores using height and compared to population means with single sample t test, and linear regression evaluating associations between CV risks and vascular measures. Results: A total of 55 children with DS participated, 28 (51%) female, mean age 14.3 years (range 8.1-18.9). Compared to NHANES, DS children were more likely to have triglycerides &gt;90 mg/dL (63% DS vs. 10% NHANES, p=&lt;0.001) and non-HDL-cholesterol &gt;120 mg/dL (54% DS vs. 22% NHANES, p = &lt;0.001). PWV and CIMT Z scores were not significantly different from general population with mean (SD) of -0.1 (1.0) and 0.27 (1.5), respectively. Univariable analysis for PWV showed significant associations with systolic blood pressure [coefficient (95% CI): 0.04 (0.02, 0.06)] and smoke exposure [1.70 (0.35, 3.05)]. No associations were found for CIMT. Multivariable models are shown in Table 1. PWV was associated with BMI percentile and smoke exposure. No associations were found for CIMT. Conclusion: Children with DS had higher rates of lipid abnormalities compared to the general population. Lipid abnormalities were not associated with CIMT or PWV, and vascular measures adjusted for height were similar to children without DS.

A Longitudinal Study of the Development of Executive Function and Calibration Accuracy.

This longitudinal study examined the development of executive function and calibration accuracy in preadolescents. This study's sample consisted of 262 students (127 females) from grades 4 (n = 91), 5 (n = 89), and 6 (n = 82) who took measures of executive function and performance calibration in a sport task three times over 20 months. A latent growth-curve modeling analysis showed a significant relationship between the rates of change of executive function and calibration accuracy. The results also showed a dynamic interplay in the development of executive function and calibration accuracy. There were significant interindividual differences in the estimated population means both in executive function and calibration accuracy and in the rate of change of executive function, but not in the rate of change of calibration accuracy. The age of the participants had a positive effect only on the estimated population mean of executive function.

A computational strategy for estimation of mean using optimal imputation in presence of missing observation.

In this study, we suggest an optimal imputation strategy for the elevated estimation of the population mean of the primary variable utilizing the known auxiliary parameters for the missing observations. Under this strategy, we suggest a new modified Searls type estimator, and we study its sampling properties, mainly bias and mean squared error (MSE), for an approximation of order one. The introduced estimator is compared theoretically with the estimators of population mean in competition under the imputation method. The efficiency conditions for the introduced estimator to be more efficient than the estimators in the competition are derived. To be sure about the efficiencies, these efficiency conditions are verified through the three natural populations. We have also conducted a simulation study and generated an artificial population with the same parameters as a natural population. The estimator with minimum MSE and the highest Percentage Relative Efficiency (PRE) is recommended for practical use in different areas of applications.

An Improvement in Regression Estimator Through Exponential Estimator Using Two Auxiliary Variables

For the case of simple random sampling, we are introducing a new regression estimator for the population mean with the supporting values of two auxiliary variables. The results for the mean square error (MSE) of the new form of regression estimator is fined. The mean square error’s results have also been checked through numerical illustration. It is observed that our introduced estimator is having less mean square error than the traditional ratio and regression estimator for two auxiliary variables.

Weighted Strategy for Estimation of Finite Population Mean in Presence of Auxiliary Information

ABSTRACT In sample designs, it is commonly recognized that using auxiliary information significantly increases an estimator’s precision. This manuscript introduces an weighted strategy for computing the finite population mean using auxiliary information in sample surveys. The equations for the mean squared error (MSE) of the proposed estimator are derived under large sampling approximation. A graphical representation of efficiency comparison is provided to show the ranges where the proposed estimator performs more efficiently. Additionally, a numerical analysis is conducted to evaluate the performance of the suggested estimator using actual datasets.

Almost Unbiased Optimum Ratio-type Estimator for Estimating Population Mean in Stratified Sampling in Presence of Non-response

In this paper we have proposed classes of ratio-type estimators for finite population mean in presence of non-response in stratified sampling. The properties of the estimators have been discussed. We have also derived optimum choices of scalar constant to reduce the bias in the estimators which make suggested classes of estimators almost unbiased. The expressions of mean square error (MSE) have been derived up to the first order of approximation. We have compared our proposed classes of estimators with natural ratio estimators and with estimator under complete response. Results are also supported by empirical study.

Standard methods to estimate strength parameters, flight activity, comb construction, and fitness of Apis mellifera colonies 2.0

This paper describes methods for estimating honey bee (Apis mellifera) colony strength by which mean population measures of adult bees and brood are obtained. Additionally, secondary measures, such as the quantity of stored honey and pollen, brood pattern, flight activity, comb construction, and the expression of visible disease or parasite symptoms are addressed. There are generally two contexts in which an investigator wishes to measure colony strength: (1) at the beginning of a study as part of manipulations to produce uniform colonies and reduce experimental error, and (2) as a response variable during or at the end of an experiment. Moreover, there are two general modes for measuring colony strength: (1) an objective mode that uses quantitative measures, and (2) a subjective mode that relies on visual estimates by one or more observers. Other parameters that do not directly measure colony strength are described because they give important indicators of colony state. These parameters include flight activity at the hive entrance, comb construction, and two proxy measures of colony fitness: production of queen cells and drone brood.

Population pharmacokinetics of everolimus in renal transplant recipients receiving long-term multiple immunosuppressive therapy

Everolimus is used for immunosuppression after renal transplantation. This study aimed to develop a population pharmacokinetic (PopPK) model of everolimus using therapeutic drug monitoring (TDM) data of patients under long-term multiple immunosuppressive therapy, including tacrolimus. To develop the model, 185 renal transplant recipients with 3358 everolimus blood concentrations during a median postoperative period of 35.3 months were included. The PopPK model is described as a one-compartment model with first-order absorption. The population mean of apparent clearance is 8.92 L/h (relative standard error = 3.6%), and this negatively correlated with the dose-normalized concentration (C/D) of tacrolimus and hematocrit value, and positively correlated with a daily dose of everolimus (i.e. TDM effect). The usefulness of dose adjustment using the final popPK model was assessed by a simulation study. The ratio of the first trough measurement within the therapeutic range of 3–8 ng/mL increased from 69.8% in the original dose to 87.9% in the individual dose calculated by the final PopPK model. The tacrolimus C/D ratio before initiating everolimus therapy and the hematocrit value were useful to estimate the initial dose of everolimus and can improve the safety and effectiveness of immunosuppressive therapy involving everolimus.

Facilitating clinical use of the Amsterdam Instrumental Activities of Daily Living Questionnaire: Normative data and a diagnostic cutoff value.

The Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q) is well validated and commonly used to assess difficulties in everyday functioning regarding dementia. To facilitate interpretation and clinical implementation across different European countries, we aim to provide normative data and a diagnostic cutoff for dementia. Cross-sectional data from Dutch Brain Research Registry (N = 1,064; mean (M) age = 62 ± 11 year; 69.5% female), European Medial Information Framework-Alzheimer's Disease 90 + (N = 63; Mage = 92 ± 2 year; 52.4% female), and European Prevention of Alzheimer's Dementia Longitudinal Cohort Study (N = 247; Mage = 63 ± 7 year; 72.1% female) were used. The generalized additive models for location, scale, and shape framework were used to obtain normative values (Z-scores). The beta distribution was applied, and combinations of age, sex, and educational attainment were modeled. The optimal cutoff for dementia was calculated using area under receiver operating curves (AUC-ROC) and Youden Index, using data from Amsterdam Dementia Cohort (N = 2,511, Mage = 64 ± 8 year, 44.4% female). The best normative model accounted for a cubic-like decrease of IADL performance with age that was more pronounced in low compared to medium/high educational attainment. The cutoff for dementia was 1.85 standard deviation below the population mean (AUC = 0.97; 95% CI [0.97-0.98]). We provide regression-based norms for A-IADL-Q and a diagnostic cutoff for dementia, which help improve clinical assessment of IADL performance across European countries.