Standard Estimation Research Articles

A cross country analysis of financial development, trade openness, population and digitalization’s impact on sustainable development

This study employs the Standard Error Estimation technique to investigate the connections between the digitalization of economy, population, trade openness, financial development, and sustainable development across 127 countries from 1990 to 2019. The findings revealed associations between financial development, population growth, trade openness, economic growth, Digitalization development, foreign direct investment (FDI), and sustainable development. Financial development negatively impacts sustainable development, suggesting that countries with advanced financial systems may struggle to maintain sustainability. Trade openness exhibits a negative association with sustainable development, implying that countries with open trade policies may face challenges in maintaining sustainability, possibly due to heightened competition or resource exploitation. These findings highlight the multifaceted relationship between economic factors and sustainable development, underscoring the importance of comprehensive policies and governance mechanisms in fostering sustainability amidst global economic dynamics.

Monitoring monthly mortality of maricultured Atlantic salmon (Salmo salar L.) in Scotland I. Dynamic linear models at production cycle level

The mortality of Atlantic salmon is one of the main challenges to achieving its sustainable production. This sector benefits from generating many data, some of which are collated in a standardized way, on a monthly basis at site level, and are accessible to the public. This continuously updated resource might provide opportunities to monitor mortality and prompt producers and inspectors to further investigate when mortality is higher than expected. This study aimed to use the available open-source data to develop production cycle level dynamic linear models (DLMs) for monitoring monthly mortality of maricultured Atlantic salmon in Scotland. To achieve this, several production cycle level DLMs were created: one univariate DLM that includes just mortality; and various multivariate DLMs that include mortality and different combinations of environmental variables. While environmental information is not collated in a standardized way across all sites, open-source remote-sensed satellite resources provide continuous, standardized estimates. By combining environmental and mortality data, we seek to investigate whether adding environmental variables enhanced the estimates of mortality, and if so, which variables were most informative in this respect. The multivariate model performed better than the univariate DLM (P = .004), with salinity as the only significant contributor out of 12 environmental variables. Both models exhibited uncertainty related to the mortality estimates. Warnings were generated when any observation fell above the 95% credible interval. Approximately 30% of production cycles and more than 50% of sites experienced at least one warning between 2015 and 2020. Occurrences of these warnings were non-uniformly distributed across space and time, with the majority happening in the summer and autumn months. Recommendations for model improvement include employing shorter time periods for data aggregation, such as weekly instead of on a monthly basis. Furthermore, developing a model that takes hierarchical relationships into account could offer a promising approach.

A technology acceptance model to predict anesthesiologists' clinical adoption of virtual reality

BackgroundVirtual reality (VR) is a novel tool with demonstrated applications within healthcare; however its integration within clinical practice has been slow. Adoption patterns can be evaluated using a technology acceptance model (TAM).The primary study aim was to use VR TAM to assess factors that influence anesthesiologists' acceptance of VR for preoperative anxiolysis. The secondary aim assessed the model's reliability. Methods109 clinical anesthesiologists at Stanford were exposed to a VR application developed as a distraction tool to reduce preoperative patient anxiety. Anesthesiologists were surveyed about their attitudes, beliefs, and behaviors as predictors of their likelihood to clinically use VR. The primary outcome assessed predictive validity using descriptive statistics, construct validity using confirmatory factor analysis, and standardized estimates of model relationships. The secondary outcome assessed reliability with Cronbach's α and composite reliability. ResultsConstruct validity and reliability was assessed, where all values established acceptable fit and reliability. Hypothesized predictors of consumer use were evaluated with standardized estimates, looking at perceptions of usefulness, ease of use, and enjoyment in predicting attitudes and intentions toward using and purchasing. Past use and price willing to pay did not predict perceived usefulness. Participants in lower age ranges had higher levels of perceived ease of use than those >55 years. ConclusionAll confirmatory factor analysis testing for construct validity had good fit. Perceptions of usefulness and enjoyment predicted an anesthesiologist's attitude toward using and intention to purchase, while perceived ease of use predicted perceived usefulness and enjoyment, attitude toward purchasing and using, and intention to use. Past use and price willing to pay did not influence perceptions of usefulness. Lower age predicted greater perceived ease of use. All scales in the model demonstrated acceptable reliability. With good validity and reliability, the VR-TAM model demonstrated factors predictive of anesthesiologist's intentions to integrate VR into clinical settings.

Not all oil types are alike in trade substitution

Motivated by the ongoing war in Ukraine and the public debate on sanctioning crude oil imports from Russia, we estimate the elasticity of substitution between different crude oil types. Using European data on country-level crude oil imports by their field of origin, we argue that crude oil is far from a homogenous good, and that the relevant substitutability for analyzing the impact of trade sanctions must account for the quality of different oil types in terms of their density and sulfur content. Distinguishing crude oil imports by their chemical composition, the elasticity of substitution between types is substantially lower than suggested by previous estimates. Our results also suggest that, by neglecting these differences in quality, standard estimates underestimate the production disruptions and price effects in crude oil refining resulting from sanctions.

Examining the Role of Organizational Culture on Citizenship Behavior: The Mediating Effects of Environmental Knowledge and Attitude Toward Energy Savings

Workplace energy conservation is vital for sustainability, as it reduces environmental harm, lowers greenhouse gas emissions, and conserves natural resources. Such procedures lead to significant financial savings, adherence to environmental standards, enhanced corporate social responsibility, and improved organizational citizenship behavior (OCB). This study investigated the impact of organizational culture (OCULT) on OCB, with environmental knowledge (EK) and attitude toward energy savings (ATES) as mediating factors. Components of the theories of planned behavior and value-belief-norm were evaluated through a structural equation model, showing that OCULT influences OCB through EK and ATES, suggesting OCULT’s direct and indirect effects on OCB. The degree to which EK and ATES mediate these effects varies, emphasizing the importance of a strong OCULT in fostering an environmentally conscious workplace. Specifically, the findings reveal that OCULT is responsible for positively impacting ATES and EK, with path coefficients of 0.587 and 0.661, respectively. OCB is positively influenced by it (coefficient of 0.228). The research indicates that knowledge is vital in improving organizational behaviors, with a significant correlation between EK and OCB (coefficient of 0.675). Mediation analysis indicates that EK mediates the relationship between OCULT and OCB (standardized estimate of 0.344), and that ATES and EK together can enhance OCB (standardized estimate of 0.078), demonstrating a robust and meaningful causal effect mediating this link. This outcome is the product of the mutually beneficial interaction among various variables. This study highlights the importance of integrating cultural considerations into knowledge management to foster a more engaged and proactive workforce, which will enhance organizational performance. As a practical implication, managers should promote energy-saving behaviors and nurture a supportive OCULT to enhance OCB. Incorporating sustainability into core organizational strategies will result in a dedicated workforce actively participating in OCB and energy-saving initiatives.

A Methodological Approach to Assembly Time Standard Estimation Based on Incomplete Characteristics of the Production Process and Using Small Databases

The problem solved in this article concerns assembly planning, which is time-consuming, but crucial in the development of mechanical products. At the product design stage there is no complete information about the manufacturing process, so it is necessary to develop an approach to help process the uncertain and incomplete information. In order to compare different product variants, the assembly time standard has to be estimated on the basis of the incomplete product and production process characteristics. This paper presents a method for estimating the assembly time standard using time classes, decision tree and evidence theory.

Modelling incidence and mortality cancer parameters with respect to GLOBOCAN 2020Age standardized world estimates

In this paper, an empirical model with the critical number of incidences and deaths for female cancers as adjustable parameters has been developed using life expectancy data from GLOBOCAN world estimates on cancer types. The model was developed based on the cumulative risk and exponential correlation techniques such that the significance of the adjustable parameters ascertains the strength of this research in two ways. First, it indicates indicates the rapid increase in female cancer morbidity with increase in the number of male cancer cases, regardless of incidence or mortality. Second, it suggests that female cancer cases may approach a virtual limiting value as male cancer cases reach extremely high levels. This projection aligns with the global population trends, indicating a proportional increase in cancer cases each year. Additionally, the cumulative risk of cancer incidences of each sex has been modelled separately against the global cumulative risk of cancer incidences of both sexes which revealed regions that have passed their inflection point and those that are yet to reach the inflection point. There was a curvature change, which indicates an inflection point coinciding with the South-Eastern Region and indicating that for the regions beyond the inflection point, the increase of the cumulative risk of cancer incidences of females against that of males is more accentuated compared with the regions before the inflection point. However, when the cumulative risk of cancer mortality of each sex is modelled separately against the global cumulative risk of cancer mortality of both sexes shows a non-linear dependence and the increase is more accentuated for males than for females. This finding indicates that cumulative risk is influenced by factors beyond the male-female population ratio. This study advances cancer studies as it provides a nuanced understanding of gender-specific cancer trends, crucial for developing targeted cancer prevention and treatment strategies. By elucidating the dynamics of cancer incidences and mortalities across different regions and sexes, the findings can inform public health policies and resource allocation to combat cancer more effectively on a global scale.

Simulations of IDCR and SOWER Antarctic minke whale abundance surveys

The International Whaling Commission (IWC) conducted abundance line‐transect surveys of Antarctic minke whales (Balaenoptera bonaerensis) during the International Decade of Cetacean Research (IDCR) between 1978/79 and 2003/04. These surveys continued throughout 2004/05–2009/10 under the Southern Ocean Whale and Ecosystem Research (SOWER) programme. Several methods used to analyse these data resulted in very different abundance estimates. In order to evaluate the accuracy of these methods, the IWC Scientific Committee recommended using simulated line‐transect data. The software package TRANSIM was therefore modified to generate simulated line‐transect data that emulated an IDRC/SOWER survey in a study area populated with whales that followed the distribution, density and behaviour of Antarctic minke whales. Simulations incorporated factors that could potentially bias resulting abundance estimates, and in order to investigate the effects of these factors on the resulting abundance estimates, simulated datasets were generated for different scenarios, each of which incorporated combinations of factors that were chosen using the mirror‐image fold‐over partial factorial method that is designed to identify factors related to biased estimates. This paper describes the programme used to generate these datasets and reports on the robustness of the statistical methods applied to analyse them. In addition, a brief discussion is included on how this information was used when the actual line‐transect data were analysed. The statistical methods investigated were the hazard probability method (OK), integrated hazard probability method (IM), spatial point independence method (SPL) and standard method. After developers analysed the simulated data, they used this information to improve their analytical methods. The results reported here focus on the performance of the most advanced versions of the OK and SPL methods compared with the standard method as of 2009. In general, SPL estimates of group density were negatively biased, OK estimates were positively biased, standard estimates were the most negatively biased and IM estimates were the least biased. Density estimates from SPL were less biased than estimates from OK in the complex scenarios that contained spatial gradients and correlations between density, location and weather conditions. The OK method was less robust when: (1) closing‐mode data collection procedures were simulated; (2) whale groups were diving in a non‐synchronous fashion; and (3) there were complex spatial patterns. In almost every case, the density estimates from the OK method were greater than the SPL methods, on average by 10–15%. These observations assisted the development of more robust analysis methods and highlighted which factors should be addressed when analysing the actual data.

Global, regional, and national drowning trends from 1990 to 2021: Results from the 2021 Global Burden of Disease Study.

Drowning is a preventable public health concern that burdens emergency care systems globally. This study comprehensively evaluated fatal drowning patterns across population, time, and geography from 1990 to 2021 to inform effective prevention strategies. Using the 2021 Global Burden of Disease Study framework and standardized estimation methods, the study analyzed global and regional drowning burden in terms of mortality, incidence, and disability-adjusted life-years (DALYs), based on population registry data and drowning-related epidemiological covariates. Global drowning incidents decreased by 33.67% from 1990 to 2021. The drowning incidence rate and mortality rate declined from 24.20 and 9.68 per 100,000 in 1990 to 10.85 and 3.48 per 100,000 in 2021, respectively. Years of life lost and DALYs rates due to drowning also decreased significantly, from 715.80 and 718.69 per 100,000 in 1990 to 197.64 and 198.92 per 100,000 in 2021. Regionally, East Asia had the highest drowning mortality (27.15% of global deaths), while the high-income Asia Pacific region experienced the highest incidence rate (21.38 per 100,000). South Asia had the greatest number of drowning deaths (75,639). Tropical Latin America showed the largest incidence decline, while high-income Asia Pacific exhibited increasing trends. Drowning disproportionately affected children under 5 and the elderly in most regions. While global drowning rates have declined, progress varies across regions. To improve prevention, focus should target low-income/middle-income countries and vulnerable populations like children and the elderly. Increased investment in safety education and rescue resources is vital to address their disproportionate risks.

A novel application of Standard Precipitation Index estimates to identify climate-related mortality risks in the Agricultural Health Study

A novel application of Standard Precipitation Index estimates to identify climate-related mortality risks in the Agricultural Health Study

Comparison of Clinical Endotracheal Tube Depths with Standard Estimates for the Stabilization of Infants with Congenital Diaphragmatic Hernia.

This study aimed to compare the clinical endotracheal tube (ETT) depth after initial stabilization of infants with congenital diaphragmatic hernia (CDH) to weight and gestational age-based depth estimates. This retrospective analysis included 58 inborn infants with left-sided CDH. We compared a standard anatomic ETT depth calculated from initial chest radiographs and the clinical depth of the ETT after adjustments to predicted depths using weight and gestational age-based estimates. The standard anatomic depth was deeper than age (standard deviation 1.29 ± 1.15 cm, p < 0.001) and weight-based (standard deviation 0.59 ± 0.95 cm, p < 0.001) estimates. The clinical ETT depth was also deeper than age (standard deviation 1.01 ± 0.77 cm, p < 0.001) and weight-based (standard deviation 0.26 ± 0.50 cm, p < 0.001) estimates. Established strategies to predict ETT depth underestimate the ideal depth in infants with left-sided CDH. These data suggest utilizing caution during initial ETT placement based on standard depth estimates for patients with CDH. · CDH patients present unique stabilization challenges.. · Standard ETT depth estimates are too shallow.. · Resuscitation teams should cautiously choose ETT depth..

A Graphical and Beta Analysis of the Effect of Increased Ethanol Production on the Volatility of Corn Prices

Increased demand for corn-based ethanol puts upward pressure on prices of corn and other commodities, such as soybeans, and possibly worsens their price volatility. The paper investigates the changes in agricultural commodities' standard deviation and beta sizes due to ethanol production in the US. Standard deviations and beta estimations are compared for the ethanol pre-expansion and expansion periods. The results indicate a high level of price volatility in the second period, which could be attributed to ethanol expansion.

Iterative Attribute Hierarchy Exploration Methods for Cognitive Diagnosis Models

Attribute hierarchies are commonly assumed to exist in many fields of psychological and educational assessment. Several theory-driven and data-driven approaches have been used to validate or explore attribute hierarchies, such as validating attribute hierarchies in the cognitive diagnostic model (CDM) through statistical hypothesis testing or even learning attribute hierarchies directly from data. A class of structural parameter standard error estimation methods for CDMs is useful for exploring attribute hierarchies, with the limitation that the information matrices of some model parameters may be unstable or singular, leading to biased hypothesis testing. An iterative method of attribute hierarchy testing was proposed to modify the original z-statistic method. The simulation study systematically compares the performance of the z-statistic and the iterative z-statistic in exploring the attribute hierarchy. The results show that the iterative z-statistic provides a better Type I error control rate and statistical power, and it partially solves the problem that the z-statistic is too conservative. In addition, the iterative z-statistic method also achieves satisfactory results on real data.

Classroom intervention to improve behavioral intention toward regular physical activity among adolescents.

During adolescence, a critical phase in human life, the groundwork for a healthful future is established. Physical inactivity poses a significant risk factor for non-communicable diseases (NCDs) and related mortality worldwide. To assess adolescents' behavioral intentions regarding regular physical activity, the Theory of Planned Behavior (TPB) examines 'Attitude,' 'Subjective norm,' and 'Perceived behavioral control.' Utilizing TPB, this study focuses on evaluating the impact of a school-based health promotion intervention on behavioral intentions toward physical activity among urban adolescents in West Bengal, India. A school-based nonrandomized controlled interventional study with parallel group design was conducted among adolescents aged between 12 and 16 years. Behavioral intention towards performing regular physical activity was determined with the measurements of the constructs from the TPB. Cluster analysis was conducted using measurements from both the intervention and control groups. Participants with higher mean scores in the constructs were classified as intenders, while the rest were considered non-intenders. The intervention's impact was evaluated by calculating the Relative Risk (RR) through a generalized linear model with robust standard error estimates, to ascertain the probability of belonging to the higher intention cluster. Following the intervention, construct-wise scores improved significantly, particularly the perceived behavioral control mean score in the intervention group. The Relative Risk (RR) of becoming an intender for regular physical activity in the intervention group was 1.24 (95% CI: 1.04-1.48) when compared to the control group. Health Promoting Schools has been recognized as a strategic and cost-effective vehicle to promote positive development and healthful living, and the current evidence suggests they can effectively reduce the emergence of significant NCD risk factor like physical inactivity. Schools must establish strong partnerships with diverse stakeholders to address barriers beyond the school environment and enhance their control over critical issues.

Adapting Seasonal Rice Cultivation Strategies for Food Security in Response to Climate Change Impacts

An in-depth examination of the effects of climate change on rice yield in China, encompassing various rice types, is crucial for ensuring the nation’s food security. This study develops an “economy-climate” theoretical model and employs Panel Corrected Standard Error Estimation (PCSE) on panel data spanning from 1978 to 2018, sourced from China’s primary grain-producing regions. The analysis delves into the impact of climate variables, including precipitation, temperature, and sunshine duration, on overall rice production and different rice types. Overall, the findings reveal a nonlinear relationship between precipitation, temperature, sunshine duration, and rice yield, characterized by an “inverted U-shaped” pattern. However, significant variations exist in the effects on different rice types across China’s main grain-producing areas. Increasing precipitation generally enhances early rice production across provinces and also augments mid-season and one-season-late rice production in the Inner Mongolia Autonomous Region, Hebei, Jilin, Heilongjiang, and Shandong Province. Conversely, it reduces mid-season and one-season-late rice output in Liaoning, Jiangsu, Anhui, Jiangxi, Henan, Hubei, and Hunan. Sichuan Province sees a rise in temperature favoring early and double-season-late rice production, unlike other provinces. For mid-season and one-season-late rice, temperature increases benefit output in Heilongjiang Province but not in other regions. Additionally, prolonged sunshine duration boosts early and double-season-late rice production across all provinces but does not have the same effect on mid-season and one-season-late rice in China’s primary grain-producing areas.

Model-assisted estimation of domain totals, areas, and densities in two-stage sample survey designs

Model-assisted, two-stage forest survey sampling designs provide a means to combine airborne remote sensing data, collected in a sampling mode, with field plot data to increase the precision of national forest inventory estimates, while maintaining important properties of design-based inventories, such as unbiased estimation and quantification of uncertainty. In this study, we present a comprehensive set of model-assisted estimators for domain-level attributes in a two-stage sampling design, including new estimators for densities, and compare the performance of these estimators with standard poststratified estimators. Simulation was used to assess the statistical properties (bias, variability) of these estimators, with both simple random and systematic sampling configurations, and indicated that 1) all estimators were generally unbiased. and 2) the use of lidar in a sampling mode increased the precision of the estimators at all assessed field sampling intensities, with particularly marked increases in precision at lower field sampling intensities. Variance estimators are generally unbiased for model-assisted estimators without poststratification, while model-assisted estimators with poststratification were increasingly biased as field sampling intensity decreased. In general, these results indicate that airborne remote sensing data, collected as an intermediate level of sampling, can be used to increase the efficiency of national forest inventories in remote regions.

Improving instrumental variable estimators with poststratification

Abstract Experiments studying get-out-the-vote (GOTV) efforts estimate causal effects of various mobilization efforts on voter turnout. However, there is often substantial noncompliance in these studies. A usual approach is to use an instrumental variable (IV) analysis to estimate impacts for compliers, here being those actually contacted by the investigators. Unfortunately, popular IV estimators can be unstable in studies with a small fraction of compliers. We explore poststratifying the data (e.g. taking a weighted average of IV estimates within each stratum) using variables that predict complier status (and, potentially, the outcome) to mitigate this. We present the benefits of poststratification in terms of bias, variance, and improved standard error estimates, and provide a finite-sample asymptotic variance formula. We also compare the performance of different IV approaches and discuss the advantages of our design-based poststratification approach over incorporating compliance-predictive covariates into the two-stage least squares (2SLS) estimator. In the end, we show that covariates predictive of compliance can increase precision, but only if one is willing to make a bias-variance trade-off by down-weighting or dropping strata with few compliers. By contrast, standard approaches such as 2SLS fail to use such information. We finally examine the benefits of our approach in two GOTV applications.

Combining Mendelian Randomization Analysis and 3D-QSAR to Investigate the Effectiveness of a New Series of Hydroxyquinolines in Osteoarthritis.

Osteoarthritis (OA) represents a persistent degenerative joint ailment. As OA advances, profound joint pain coupled with diminished joint function inflicts substantial physical distress and psychological strain on patients. Presently, pharmacological solutions for arthritis remain limited, primarily encompassing analgesics and joint replacement surgical procedures. Hence, non-operative strategies to mitigate osteoarthritis progression have captured significant attention in orthopedic research. This study aims to discern a definitive causal linkage between ADAMTS-4/5 and osteoarthritis through Mendelian randomization analysis. Moreover, it seeks to anticipate the therapeutic efficacy of a suite of emergent hydroxyquinolines for osteoarthritis using the Quantitative Structure-Activity Relationship (QSAR) methodology. Within this study, genetic variants specific to knee osteoarthritis were procured as exposure variables from a genome-wide association study (GWAS). Genetic variant data for ADAMTS-4/5 served as the endpoint to evaluate the causal nexus employing univariate Mendelian randomization. This analysis underpins the hypothesis that ADAMTS-4/5 presents a promising therapeutic target for osteoarthritis management. The suppressive properties of novel hydroxyquinolines against ADAMTS-4/5 were subsequently examined through conformational analyses, underscoring the potential of these compounds as therapeutic candidates for osteoarthritis. IVW outcomes from the Mendelian randomization revealed a significant association of KOA (OR: 1.1675, 95% CI: 1.0003-1.3627, P = 0.0495) with ADAMTS-5. However, KOA (OR: 1.0801, 95% CI: 0.9256-1.2604, P = 0.3278) displayed no evident connection with ADAMTS-4. Notably, the instrumental variables manifested neither heterogeneity nor horizontal pleiotropy. In this research endeavor, 16 pharmacological models were formulated via the CoMSIA method within 3D conformational relationship evaluations. A synergistic interplay of hydrophobic, spatial, and hydrogen-bonded receptor domains emerged as the most predictively potent. The cross-validation coefficient q2 for the optimum model stood at 0.716, with a principal component score of 5, a regression coefficient r2 of 0.971, a standard estimation error of 0.351, and an f-value of 156.951. Such metrics intimate the commendable predictive prowess of our devised CoMSIA models. The research unearthed a robust causal interrelation between ADAMTS-5 and osteoarthritis via Mendelian randomization. Furthermore, a credible drug model targeting ADAMTS-5 was constructed. Collectively, these findings illuminate a path forward in the pursuit of target-specific drugs for osteoarthritis management in subsequent investigations.

When Implementing the Presynch-11/Ovsynch Reproductive Management Program, the Fertility of Lactating Dairy Cows Improved When They Received Timed Artificial Insemination Compared with the Inclusion of Estrus Detection.

Artificial insemination 12 h following observed standing estrus is a standard estimate of the fertility levels of cattle. The main objective of this study was to determine if controlling ovarian development with a fertility program could alter the fertility of lactating dairy cows. Lactating dairy cows (n = 1356) 60-66 days in milk (DIM) were randomly assigned to receive timed AI following Presynch-11/Ovsynch (Presynch-11) or a combination of estrus detection and timed AI using the Presynch-11/Ovsynch program (Estrus + TAI). Cows in standing estrus, following the first two cloprostenol sodium (CLO) injections, in the Estrus + TAI group were artificially inseminated using the AM/PM rule. Cows in this group that were not observed in standing estrus received Ovsynch and TAI beginning 11 d after the second CLO injection. Cows in the Presynch-11 group received two injections of 500 µg CLO 14 d apart but were not observed for estrus. The first GnRH (100 µg) of Ovsynch was administered 11 d following the second CLO injection. All cows in the Presynch-11-Ovsynch group received TAI following Ovsynch. Cows in this treatment were then assigned randomly to receive either CLO or dinoprost 7 d following the first GnRH of Ovsynch. The final GnRH of Ovsynch was administered 56 h later and TAI 16 h after the final GnRH. Pregnancies/AI (P/AI) were greater (p < 0.001) for the Presynch-11 group compared with the Estrus + TAI group (45 vs. 31%). Primiparous cows had greater fertility following observed standing estrus compared with multiparous cows. Days in milk at the first AI were greater (p < 0.01) in the Presynch-11 group vs. the Estrus + TAI group (98 vs. 80) but less variable (p < 0.01). The range of DIM at the first AI was 95 to 101 in the Presynch-11 group and 60 to 101 in the Estrus + TAI group. Within the Presynch-11 group, there were no differences in the rate of luteolysis or P/AI for the prostaglandin type at the final PGF2α of Ovsynch. Multiparous cows treated with Presynch-11 had >60% chance of pregnancy compared with multiparous cows receiving AI following standing estrus. In summary, lactating dairy cows receiving timed AI following the Presynch-11/Ovsynch program had improved fertility compared with a group of cows that received AI following standing estrus or, if not observed in estrus, timed AI. This comparison indicated that controlling ovarian development with GnRH and PGF2α positively impacted the fertility of lactating dairy cows.

Adjusted Design Effect Model for Longitudinal Survey Data

In this research, we propose a longitudinal adjusted design effect model that integrates both the design effect and time effect, offering reliable estimators of variance. We investigate the asymptotic properties of certain types of estimators. Through simulation studies, we show the exceptional performance of the proposed method, demonstrating its superiority in producing accurate standard error estimators compared to existing approaches.