Additive Adjustments Research Articles

Digital Protocol to Record Occlusal Analysis in Prosthodontics: A Pilot Study.

Background: Digital technologies enable the accurate replication of occlusion, which is pivotal for stability in maximum intercuspation and dynamic occlusion. CAD softwares generates standardized occlusal morphologies requiring significant adjustments. The consideration of individual mandibular movements during restoration leads to better functional integration. This pilot study evaluates the efficacy of a novel, fully digital protocol for occlusal analysis recording in prosthodontics. Methods: Patients needing single or multiple metal-free restorations were included. Teeth underwent horizontal finish line preparation, while restorations on implants were either directly screwed or used multi-unit abutments. A digital impression (Trios 3 Intraoral Scanner) captured the mouth's elements. Dynamic occlusion was recorded via Patient Specific Motion (PSM). After the placement and functionalization of temporary restorations, subsequent scans included various elements, and CAD software (Dental system) was used for the restoration design. Restorations were milled in monolithic zirconia, pressed from CAD/CAM-milled wax, and sintered. Results: An evaluation of 52 restorations in 37 patients indicated high accuracy in restorations manufactured via the fully digital workflow. Monolithic zirconia was predominantly used. Subtractive (17.3%) and additive (7.7%) occlusal adjustments were mainly chairside. Conclusion: This study underscores the efficacy of meticulous verification measures and a centric contact system in reducing the need for clinical occlusal refinements in prosthetic restorations.

Updating test-day milk yield factors for use in genetic evaluations and dairy production systems: a comprehensive review.

Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. This paper offers an exhaustive review of daily milk yields, the foundation of lactation records. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive additive correction factors (ACF) and multiplicative correction factors (MCF). An ACF model provides additive adjustments to two times AM or PM milk yield, which then becomes the estimated daily yields, whereas an MCF is a ratio of daily yield to the yield from a single milking. Recent studies highlight the potential of alternative approaches, such as exponential regression and other nonlinear models. Biologically, milk secretion rates are not linear throughout the entire milking interval, influenced by the internal mammary gland pressure. Consequently, nonlinear models are appealing for estimating daily milk yields as well. MCFs and ACFs are typically determined for discrete milking interval classes. Nonetheless, large discrete intervals can introduce systematic biases. A universal solution for deriving continuous correction factors has been proposed, ensuring reduced bias and enhanced daily milk yield estimation accuracy. When leveraging test-day milk yields for genetic evaluations in dairy cattle, two predominant statistical models are employed: lactation and test-day yield models. A lactation model capitalizes on the high heritability of total lactation yields, aligning closely with dairy producers' needs because the total amount of milk production in a lactation directly determines farm revenue. However, a lactation yield model without harnessing all test-day records may ignore vital data about the shapes of lactation curves needed for informed breeding decisions. In contrast, a test-day model emphasizes individual test-day data, accommodating various intervals and recording plans and allowing the estimation of environmental effects on specific test days. In the United States, the patenting of test-day models in 1993 used to restrict the use of test-day models to regional and unofficial evaluations by the patent holders. Estimated test-day milk yields have been used as if they were accurate depictions of actual milk yields, neglecting possible estimation errors. Its potential consequences on subsequent genetic evaluations have not been sufficiently addressed. Moving forward, there are still numerous questions and challenges in this domain.

Excesso de mortes durante a pandemia de COVID-19 em Mato Grosso, Brasil

Abstract This article aims to estimate the excess of deaths in the years 2020 and 2021 in Mato Grosso state, in the state capital and in the countryside, according to gender and age group. Data was extracted from DATASUS/Ministry of Health website for the period from 2015 to 2020 and from the website of the State Department of Health - Data Warehouse System (DW) for 2021. Non-fetal deaths by natural causes of residents in Mato Grosso were analyzed and the analyses were broken down into countryside and state capital (Cuiabá). The variables selected were age group, gender, month of occurrence, and underlying cause of death. Excess mortality was calculated using generalized additive quasi-Poisson model adjustments with correction for overdispersion. A 30% excess of deaths was identified in 2020, with the state capital recording the highest estimate, in older age groups, and between the months of July and September. In 2021, the expected number of deaths was 57% higher, with the double in the younger age groups in the countryside. The study showed different demographic profiles of excess deaths in the years 2020 and 2021 during COVID-19 pandemic and distinct patterns between countryside and state capital, suggesting inequalities that may have caused impact on different risks.

Statistical Methods Revisited for Estimating Daily Milk Yields: How Well do They Work?

Cost-effective milking plans have been adapted to supplement the standard supervised twice-daily monthly testing scheme since the 1960s. Various methods have been proposed to estimate daily milk yields (DMY), focusing on yield correction factors. The present study evaluated the performance of existing statistical methods, including a recently proposed exponential regression model, for estimating DMY using 10-fold cross-validation in Holstein and Jersey cows. The initial approach doubled the morning (AM) or evening (PM) yield as estimated DMY in AM-PM plans, assuming equal 12-h AM and PM milking intervals. However, in reality, AM milking intervals tended to be longer than PM milking intervals. Additive correction factors (ACF) provided additive adjustments beyond twice AM or PM yields. Hence, an ACF model equivalently assumed a fixed regression coefficient or a multiplier of “2.0” for AM or PM yields. Similarly, a linear regression model was viewed as an ACF model, yet it estimated the regression coefficient for a single milk yield from the data. Multiplicative correction factors (MCF) represented daily to partial milk yield ratios. Hence, multiplying a yield from single milking by an appropriate MCF gave a DMY estimate. The exponential regression model was analogous to an exponential growth function with the yield from single milking as the initial state and the rate of change tuned by a linear function of milking interval. In the present study, all the methods had high precision in the estimates, but they differed considerably in biases. Overall, the MCF and linear regression models had smaller squared biases and greater accuracies for estimating DMY than the ACF models. The exponential regression model had the greatest accuracies and smallest squared biases. Model parameters were compared. Discretized milking interval categories led to a loss of accuracy of the estimates. Characterization of ACF and MCF revealed their similarities and dissimilarities and biases aroused by unequal milking intervals. The present study focused on estimating DMY in AM-PM milking plans. Yet, the methods and relevant principles are generally applicable to cows milked more than two times a day.

Revealing information in auctions: The optimal auction versus the second-price auction

We study the optimal information disclosure policy in the optimal auction and the second-price auction when the seller has information that additively adjusts the independent private values of the bidders. In this setting, information revelation could change the allocation of the good in both types of auctions. However, in the optimal auction, the change in allocation makes the revenue function convex in the additive adjustments, so the seller should always reveal information. In contrast, in the second-price auction, the change in allocation makes the revenue function non-convex, in which case the seller might benefit from withholding information.

Performance of Existing Methods in Baselining Demand Response From Commercial Building HVAC Fans

Abstract Commercial buildings generally have large thermal inertia and thus can provide services to power grids (e.g., demand response (DR)) by modulating their heating, ventilation, and air conditioning (HVAC) systems. Shifting consumption on timescales of minutes to an hour can be accomplished through temperature setpoint adjustments that affect HVAC fan consumption. Estimating the counterfactual baseline power consumption of HVAC fans is challenging but is critical for assessing the capacity and participation of DR from HVAC fans in grid-interactive efficient buildings (GEBs). DR baseline methods have been developed for whole-building power profiles. This study evaluates those methods on total HVAC fan power profiles, which have different characteristics than whole-building power profiles. Specifically, we assess averaging methods (e.g., Y-day average, HighXofY, and MidXofY, with and without additive adjustments), which are the most commonly used in practice, and a least squares-based linear interpolation method recently developed for baselining HVAC fan power. We use empirical submetering data from HVAC fans in three University of Michigan buildings in our assessment. We find that the linear interpolation method has a low bias and by far the highest accuracy, indicating that it is potentially the most effective existing baseline method for quantifying the effects of short-term load shifting of HVAC fans. Overall, our results provide new insights on the applicability of existing DR baseline methods to baselining fan power and enable the more widespread contribution of GEBs to DR and other grid services.

Heterogeneity in mortality: a survey with an actuarial focus

Heterogeneity in mortality is due to differences among the individuals, which are caused by various risk factors. Some risk factors are observable while others are unobservable. The set of observable risk factors clearly depends on the type of population addressed. The impact of observable risk factors on individual mortality, in particular when they also constitute “rating factors” in the calculation of premiums and other actuarial values, is usually expressed approximately, according to some pragmatic approach. For example, additive or multiplicative adjustments to the average age-specific mortality are frequently adopted. Heterogeneity due to unobservable risk factors can conversely be expressed by adopting the concept of individual “frailty”, which, however, can be interpreted and consequently modeled in several ways, according to the causes which are considered as originating the frailty itself: congenital characteristics, environmental features, lifestyle aspects, etc. In this paper, we provide a survey of scientific contributions on heterogeneity in mortality, focusing on modeling both observable and unobservable heterogeneity. We start with an overview of methodological contributions to heterogeneity and frailty modeling, coming from both the demographical and the actuarial context. We then shift to contributions analyzing the impact of frailty, in its various interpretations, on the results (cash flows, profits, etc.) of life insurance and life annuity portfolios and related risk profiles.

Relationship between sarcopenic obesity and metabolic syndrome among Japanese elderly who underwent a comprehensive health checkup

Objective. We assessed the relationship between sarcopenic obesity (SO) and metabolic syndrome (MetS) in Japanese elderly. Design. Cross-sectional observational study. Setting. Comprehensive health checkup center. Participants. A total of 235 individuals (95 men and 140 women; mean age, 73.2 ± 6.0 years) underwent comprehensive health checkups and examination of whole-body composition using dual-energy X-ray absorptiometry. Sarcopenia was defined by height-adjusted skeletal muscle mass, cutoff points of <7.0 kg/m2 for men and <5.4 kg/m2 for women. Obesity was defined by total-body fat percentage, cutoff points of ≥25% for men and ≥30% for women. The diagnostic criteria of MetS defined by the Japanese Society of Internal Medicine were used. Main outcome measures. Multivariate logistic regression analyses were performed to identify the risk for MetS associated with SO. Results. The prevalences of sarcopenia, obesity, and SO were 20%, 31%, and 18%, respectively. The prevalence of MetS at-risk was 31%, which was significantly higher in men than in women (42% vs. 24%, respectively; p < 0.05). Multivariate logistic regression analyses revealed that obesity and SO were associated with increased risk for MetS at-risk in the age- and sex-adjusted model (odds ratio [OR] and 95% confidence interval [CI] for obesity: OR, 9.69; 95% CI, 4.13-22.75; for SO: OR, 3.12; 95% CI, 1.23-7.94). After additive adjustments with body mass index (BMI), smoking, drinking, and physical activity, only SO showed a marginally positive association with the risk for MetS at-risk (OR, 3.02; 95% CI, 0.99-9.22). Conclusions. SO showed a greater association with the risk for MetS than obesity alone, independent of BMI and other confounders.

Testing a weather generator for downscaling climate change projections over Switzerland

ABSTRACTClimate information provided by global or regional climate models (RCMs) are often too coarse and prone to substantial biases for local assessments or use in impact models. Hence, statistical downscaling becomes necessary. For the Swiss National Climate Change Initiative (CH2011), a delta‐change approach was used to provide daily climate scenarios at the local scale. Here, we analyse a Richardson‐type weather generator (WG) as an alternative method to downscale daily precipitation, minimum and maximum temperature. The WG is calibrated for 26 Swiss stations and the reference period is 1980–2009. It is perturbed with change factors derived from RCMs (ENSEMBLES) to represent the climate of 2070–2099 assuming the SRES A1B emission scenario. The WG can be run in multi‐site mode, making it especially attractive for impact‐modellers that rely on a realistic spatial structure in downscaled time‐series. The results from the WG are benchmarked against the original delta‐change approach that applies mean additive or multiplicative adjustments to the observations.According to both downscaling methods, the results reveal mean temperature increases and a precipitation decrease in summer, consistent with earlier studies. For the summer drying, the WG indicates primarily a decrease in wet‐day frequency and correspondingly an increase in mean dry spell length of between 18 and 40% at low‐elevation stations. By definition, these potential changes cannot be represented by a delta‐change approach. In winter, both methods project a shortening of the frost period (−30 to −60 days) and a decrease of snow days (−20 to −100%). The WG demonstrates though, that almost present‐day conditions in snow‐days could still occur in the future. As expected, both methods have difficulties in representing extremes. If users focus on changes in temporal sequences and need a large number of future realizations, it is recommended to use data from a WG instead of a delta‐change approach.

Some classes of modified ratio type estimators in sample surveys

In this paper some classes of modified ratio type estimators with additive and multiplicative adjustments made to the simple mean per unit estimator and classical ratio estimator are suggested to obtain more efficient ratio type estimators compared to the classical one. Their biases and mean square errors are obtained and compared with first order approximations.

Some Classes of Modified Ratio Type Estimators in Sample Surveys

Abstract In this paper some classes of modified ratio type estimators with additive and multiplicative adjustments made to the simple mean per unit estimator and classical ratio estimator are suggested to obtain more efficient ratio type estimators compared to the classical one. Their biases and mean square errors are obtained and compared with first order approximations.

Sensitivity Analysis in Non-Inferiority Trials with Residual Inconstancy After Covariate Adjustment

SummaryA major issue in non-inferiority trials is the controversial assumption of constancy, namely that the active control has the same effect relative to placebo as in previous studies comparing the active control with placebo. The constancy assumption is often in doubt, which has motivated various methods that ‘discount’ the control effect estimate from historical data as well as methods that adjust for imbalances in observed covariates. We develop a new approach to deal with residual inconstancy, i.e. possible violations of the constancy assumption due to imbalances in unmeasured covariates after adjusting for the measured covariates. We characterize the extent of residual inconstancy under a generalized linear model framework and use the results to obtain fully adjusted estimates of the control effect in the current study based on plausible assumptions about an unmeasured covariate. Because such assumptions may be difficult to justify, we propose a sensitivity analysis approach that covers a range of situations. This approach is developed for indirect comparison with placebo and effect retention, and implemented through additive and multiplicative adjustments. The approach proposed is applied to two examples concerning benign prostate hyperplasia and human immunodeficiency virus infection, and evaluated in simulation studies.

Seasonal Adjustment with theRPackagesx12andx12GUI

The X-12-ARIMA seasonal adjustment program of the US Census Bureau extracts the different components (mainly: seasonal component, trend component, outlier component and irregular component) of a monthly or quarterly time series. It is the state-of-the- art technology for seasonal adjustment used in many statistical offices. It is possible to include a moving holiday effect, a trading day effect and user-defined regressors, and additionally incorporates automatic outlier detection. The procedure makes additive or multiplicative adjustments and creates an output data set containing the adjusted time series and intermediate calculations. The original output from X-12-ARIMA is somehow static and it is not always an easy task for users to extract the required information for further processing. The R package x12 provides wrapper functions and an abstraction layer for batch processing of X-12-ARIMA. It allows summarizing, modifying and storing the output from X-12-ARIMA within a well-defined class-oriented implementation. On top of the class-oriented (command line) implementation the graphical user interface allows access to the R package x12 without requiring too much R knowledge. Users can interactively select additive outliers, level shifts and temporary changes and see the impact immediately. The provision of the powerful X-12-ARIMA seasonal adjustment program available directly from within R, as well as of the new facilities for marking outliers, batch processing and change tracking, makes the package a potent and functional tool.

Improved additive adjustments for the LR/ELR test statistics

The Bartlett adjustment, being a simple adjustment through division by the expected value of the test statistic, is commonly used as a general statistical tool to reduce the error of the chi-squared approximation of parametric/empirical likelihood ratio (LR/ELR) test statistic. In this paper, some improved test statistics in the additive forms are presented, whose errors of the chi-squared approximation are o ( N − 1 ) , as in the case of the traditional multiplicative Bartlett adjustment, where N is the sample size. By deriving the N − 1 -difference of the power functions of two tests under a sequence of local alternatives, it is shown that none of several adjustments of the LR/ELR test statistic is uniformly superior. The results are numerically illustrated on specific examples.

There Are No Sheep in Post-Structuralism

Progressive white scholars often proceed as if “incorporating race” and culture into our work automatically destabilized the whiteness of our intellectual and pedagogical practices. Diligently, we add brown and black references to our publications, assign readings by scholars of color, and weave material about race into our lectures. Many of our attempts to decenter whiteness, however, effectively reinstall white authority. Additive adjustments underscore the foundational white assumptions that organized our research questions or syllabus in the first place — as when we begin by defining what counts as feminist or queer pedagogy, and then blend in race themes to ensure our inclusiveness. Even when race and culture are central to our work, chances are that those of us who are white — and some who are not — appeal to white-referenced ways of naming and framing race and culture. As Wendy Kozol asks, “what do we mean by ‘race’?” What do we mean by “culture”?

A. Buccianti, G. Mateu-Figueras and V. Pawlowsky-Glahn (eds): Compositional data analysis in the geosciences: from theory to practice

It is already 25 years since the publication of John Aitchison’s seminal paper on the statistical analysis of compositional data, which finally provided a solution to the problem identified by Karl Pearson in the nineteenth century of spurious correlation when there is a constant sum constraint. Log-ratio transformations provide a way to ‘‘escape’’ the constraint of a constant sum in a coherent way, which enables the consistent analysis of sub-compositions. It is surprising that such a fundamental advance has still not been fully embraced by scientific disciplines that deal frequently with compositional data, such as geology. Given this lack of adoption of such a fundamental advance, a book like this one is especially invaluable as it provides an excellent balance between the theoretical advances of the 20 years since John Aitchison’s book and detailed analysis of practical problems that shows that log-ratio methods provide more meaningful solutions in practice. Overall, there are four theoretical chapters, eight chapters that apply the methods to real datasets and three chapters, which cover the software available for compositional data analysis. Although the book was initially based on conference contributions, the chapters are quite consistent in notation and level, suggesting hard work by the editors! The introductory chapter by Pawlowsky-Glahn and Egozcue explains how log-ratio transformations solve the spurious correlations of Pearson and covers the advances since the Aitchison book, including isometric-log-ratio (ilr) transformations, which provide an alternative to the additive-log-ratio (alr) and centred-log-ratio (clr) transformations. However, arguably, the major benefits of the ilr transformations are mathematical (an orthogonal basis) and geometric (neat link with the implied geometry on the simplex) rather than statistical as the results transformed back to the simplex are not affected by the choice of transformation and the ilr transformations can be harder to interpret. Centering the data at the geometric mean, as a special case of the perturbation operation, is shown to help in visualization, together with biplots, which provide a useful graphical summary of the variation in the simplex. The second theoretical chapter by Egozcue and Pawlowsky-Glahn provides a detailed description of the geometry on the simplex including the distance metric, which is simple for the clr and ilr representation/transformations. While this chapter is interesting and important from a mathematical and geometrical perspective it is not clear that it provides additional statistical or geological insight. The third theoretical chapter by Martin-Fernandez and Thio-Henestrosa discusses possible solutions to the problem of zero components, treating the zeros as though the true value is missing due to detection limits rather than a complete absence of the component. They examine the possibility of using non-parametric imputation following the methods of Rubin and Little for missing data and conclude that multiplicative adjustments are superior to additive or simple adjustments, although they emphasize the necessity of sensitivity analysis to examine the robustness in the context of the analysis selected. They also claim that artificial correlation is caused when there is more than one component with zeros and non-parametric methods of imputation are used, but it seems that this outcome reflects the use of a specific non-parametric method, rather than a weakness of non-parametric methods J. Bacon-Shone (&) Social Sciences Research Centre, The University of Hong Kong, Pokfulam Road, Hong Kong e-mail: johnbs@hku.hk

Deriving Lactation Yields from Test-Day Yields Adjusted for Lactation Stage, Age, Pregnancy, and Herd Test Date

Lactation records for milk, fat, and protein yields were calculated from test-day data adjusted for the effects of lactation stage, age, previous days open, days pregnant, and test-day class (herd, test date, and milking frequency). Those lactation records reflect the improved accounting of environmental effects from a test-day model and can be combined with historical lactation records. Test-day data were adjusted with existing lactation multiplicative adjustments to maintain variance characteristics. Then additive adjustments for lactation stage, age, previous days open, and days pregnant were applied. The current multiplicative adjustments for previous days open were not applied because its effect was expected to differ by lactation stage. To remove genetic differences, the estimated breeding value from the previous evaluation divided by 305 was subtracted. Effects of test-day class, and permanent environment within and across parities were estimated within herd. The effect of test-day class was subtracted from adjusted test-day yield, and the breeding value restored. Those deviations then were combined with the best prediction procedure into a lactation measure. Heritabilities and repeatabilities of lactation records that were adjusted for test-day class were higher than for current lactation records. The adjusted records should improve the accuracy of evaluations and allow the use of test-day data as well as provide for the continued use of historical data when test-day data are not available.

Rank-one and rank-two departures from symmetry

ten Berge (Comput. Statist. Data Anal. 24, 1997, 357–366) distinguished between rank-one and rank-two departures from symmetry. A re-examination suggests that there is no substantive difference between the two approaches unless the type of symmetry is constrained in some way. The relationship between rank and dimensionality in the context of asymmetry is clarified. Implications for diagnostic methods for distinguishing between different models of asymmetry are discussed, paying special attention to additive adjustments to a distance matrix. O what a tangled web we weave when first we practise to deceive. (Sir Walter Scott, Marmion). There's no such thing as a free lunch. (Well-known saying).

Animal model genetic evaluation of type traits for five dairy cattle breeds.

Abstract A system to calculate genetic evaluations based on an animal model was developed for final score (single-trait model) and 15 linear type traits (multitrait model) of Ayrshires, Brown Swiss, Guernseys, Jerseys, and Milking Shorthorns. (Co)variance components were estimated from appraisals that were scored during 1988 and later and that included all linear traits. The model for (co)variance components included fixed effects for interactions of herd, appraisal date, and parity; parity and appraisal age; and parity and lactation stage. Random effects were included for permanent environment, animal, and residual. A canonical transformation was used with approximate diagonalization. Data for estimating breeding values included appraisals from 1980. Effects for appraisal age and lactation stage were defined within appraisal year group. The model for calculation of breeding values also included a random effect of interaction between herd and sire. Solutions for appraisal age from a preliminary analysis were smoothed with a quadratic curve to generate additive age adjustments by month for appraisal age, parity, and appraisal year group. Correlations of solutions from this model and from the former USDA sire model for bulls that were born during 1975 or later and that had 20 daughters were highest (generally ≥0.90) for Guernseys and were lowest (generally

Effects of Age on Milk Yield: Time Trends and Method of Adjustment

Multiplicative adjustment factors for age that were used in the US from 1974 through 1994 were developed from performance records of the 1960s. Rapid genetic improvement and improved management may have altered the relationship between lactation yield and calving age. The purpose of this study was 1) to examine changes in age effects on lactation milk yield over time; 2) to compare multiplicative, additive, or a combined method to adjust records for calving age; and 3) to examine effect of adjustment method on estimates of genetic trend. Data were complete lactation records (n = 765,413) of 383,011 Holsteins from Wisconsin DHI. An animal model was used with fixed effects for herd-year and month of calving by period by age-lactation class and with random effects for EBV, permanent environment, and error. Correction factors were applied additively, multiplicatively, and in combinations. The combination of additive and multiplicative adjustments that minimized heterogeneity of standard deviations within age class was considered to be optimal. The increase in yield that was associated with advancing age and lactation number was greater during recent years than during earlier years, Heterogeneity of standard deviations within age class was nearly minimum with additive adjustment for lactations 1 to 3. For lactations 4 and 5, heterogeneity was not sensitive to method of adjustment; however, multiplicative adjustment was near the optimum. The genetic trend from records with multiplicative adjustment was substantially greater than additive and optimal adjustments, especially during recent years.