Intuitive Estimates Research Articles

Enhanced formula-based method to estimate burnup, initial enrichment, and cooling time using limited gamma-ray source data of reference PWR spent nuclear fuel assemblies

This study proposes a framework to develop a model for estimating burnup (BU), initial enrichment (IE), and cooling time (CT) of spent nuclear fuel (SNF) overcoming the limitations of existing models in determining IE. Database (DB) of gamma-ray sources for PWR SNF was generated using ORIGAMI. Target gamma radionuclides were selected based on the ease and credibility of measurement. The concept of Product of Powers of Radioactivity (PPR) was suggested, and PPRs for modeling were selected through evaluation of all possible PPRs. Functions established for PPRs were applied to estimate BU, IE, and CT (BIC) of PWR SNF assemblies. Optimized PPR combinations for estimating BU, IE, and CT respectively, as well as three variables simultaneously with the highest accuracy, were suggested. The estimation model proposed in this study enhanced IE estimations, maintaining the reliability of BU and CT estimations. The above framework enables construction of intuitive BIC estimation models using only a small amount of data. This can help hasten the development of estimation models for SNF from various reactor types. Consequently, it is expected to contribute to the safety and safeguards of SNF management by improving the verification that SNF meets acceptance criteria and conforms to the declared information.

Distinguishing Intuitive and Deliberative Reasoning Regarding Different Aspects of Science Classroom Discourse

Purpose The current study aimed to identify the distances between a science teacher's intuitive reasoning and researchers’ systematic analysis of various aspects of science classroom discourse. An expert science teacher, given the pseudonym Jake, engaged in self-reflective processes regarding different aspects of classroom discussions to assess the variations and similarities between the teacher's intuitive estimations and the researchers’ data-based reasoning. Design/Approach/Methods Data collection involved in-depth interviews and video recording. Six aspects of science classroom discourse were considered to elucidate the distances: structure, interactional pattern, verbal sequence, typology, cognitive demand, and communicative approaches. Findings The quantitative distances between Jake's estimations and systematic observations of the six aspects of science classroom discourse varied across different aspects of the observed science lesson. While no consistent pattern was observed in the distances, there were increasing and decreasing gaps between the different aspects of science classroom discourse. Originality/Value Considering the contemporary arguments on human cognition and the professional development of science teachers, potential reasons for the differences were explored. Educational recommendations were provided, particularly regarding supporting science teachers’ journey toward becoming reflective inquirers or researcher-teachers.

Farmland quality assessment using deep learning and UAVs

The transformation of agricultural landscapes due to the intensification of farming practices has placed a major threat on habitat and species diversity. Stopping the loss of biodiversity is addressed in various international treaties and adequate strategies are required to assess the initial status of biodiversity and to measure the success of policies. While traditional methods of field-based biodiversity monitoring are time consuming, costly, and highly surveyor-dependent, recent advances in remote sensing and machine learning enable a systematic, more general and cost-effective monitoring. Accordingly, this study investigated whether large-scale biodiversity monitoring campaigns such as the European Monitoring of Biodiversity in Agricultural Landscapes (EMBAL) could be supported by the use of Unmanned Aerial Vehicle (UAV) based remote sensing and Convolutional Neural Networks (CNNs). For this purpose, the Structural Nature Value (SNV) indicator was proposed, which allows an intuitive estimation of the species richness of landscape sections. Subsequently, a CNN was trained to directly predict the SNV. For the accuracy assessment, a survey was conducted in which participants were asked to assess the SNV for various landscape patches, both to obtain an independent test set, and to evaluate the intuitiveness of the concept. The CNN achieved a mean weighted F1-score of 0.81 with an overall accuracy of 81 %. It was shown that the CNN can provide promising results to not only aid large-scale biodiversity monitorings, but to enhance the quality of their results through the automated analysis of fine-resolution UAV imagery.

Dynamic hysteresis effects

We study how the output gap affects potential output over time—i.e., the dynamic hysteresis effect. To do so, we introduce novel unobserved components (UC) models that consider hysteresis as a sequence of lagged effects, thus separating the long-run recession-induced adverse effects from other trend-cycle interactions. The proposed models nest several existing UC models in the literature and accommodate two key characteristics of output dynamics: non-neutrality in the long-run and time-to-build effects. Using Bayesian estimation methods, we find robust evidence supporting the presence of hysteresis effects after the 1970s, with the negative long-run effect of the Global Financial Crisis and the COVID-19 recessions robustly identified. Via Bayesian model averaging, we provide precise and intuitive output gap estimates that highlight the relationship between business cycle fluctuations and the decline in economic growth. Our findings indicate that output trend-cycle decompositions that do not consider hysteresis effects can alter stabilization policy trade-offs.

Statistical Methods for Selective Biomarker Testing

AbstractBiomarkers are critically important tools in modern clinical diagnosis, prognosis, and classification/prediction. However, there are fiscal and analytical barriers to biomarker research. Selective Genotyping is an approach to increasing study power and efficiency where individuals with the most extreme phenotype (response) are chosen for genotyping (exposure) in order to maximize the information in the sample. In this article, we describe an analogous procedure in the biomarker testing landscape where both response and biomarker (exposure) are continuous. We propose an intuitive reverse-regression least squares estimator for the parameters relating biomarker value to response. An expression for robust standard error and corresponding confidence interval are derived. A simulation study is used to demonstrate that this method is unbiased and efficient relative to estimates from random sampling when the joint normal distribution assumption is met, and to compare the estimator to an alternative under a related sampling design. We illustrate application of proposed methods on data from a chronic pain clinical trial.

Грот Буздужаны 1 и проблемы среднего палеолита в междуречье Днестра и Прута

This paper is a response to the monograph by L. B. Vishnyatsky and V. A. Burlacu “Buzdujeni 1. Multi-layer cave site of the Middle Paleolithic in the Prut River basin”. This paper criticizes a number of conclusions of the authors of the monograph, including the definition of the stone industry from this site. The earlier definition of industry as a denticulated Mousterian was based on the use of F. Bordes’ technique. The authors of the monograph, on the other hand, ignored Bordes’s method, using their own, devoid of exact definitions and rather based on intuitive estimates. According to them, all denticulated tools are regarded as pseudo-retouched stone artifacts. According to the authors of the monograph, this retouching occurred naturally in the layer. This point of view does not take into account the totality of facts, just ignores them. The counterarguments given in the paper point to the reality of the denticulated Mousterian. The monograph under consideration also proposes the idea of the simultaneous formation of all grottos in Moldavia with Middle Palaeolithic stone tools. This idea is based on an erroneous assumption about the simultaneous geomorphological formation of these grottos in the Riss-Würm interglacial. The authors of the monograph ignored the fact of the existence of a well-studied grotto with older deposits — Duruitoarea Veche.

Major Challenges for Palliative Care in Oncohematology: Analysis of the “Surprise Question” in a Reference Center

Introduction: Despite advances in the treatment of hematologic malignancies (HM), a significant number of patients still present with high burden of symptoms, and many still die from their diseases. Adequate integration of palliative care (PC) shows strong potential to improve quality of life of this population. However, PC strategies are underutilized due to several barriers in the context of high prognosis uncertainty. Therefore, it is crucial to implement practical tools and triggers for opportune discussions concerning goals of care and specialist PC consultations. The “Surprise-Question” (SQ) “Would you be surprised if this patient died in the next 12 months?” is a simple and feasible instrument for intuitive estimation of mortality among patients with different types of advanced disease with the aim to improve prognostic awareness among healthcare professionals. In 2016 this instrument was introduced in the American Society of Clinical Oncology (ASCO) Integration of Palliative Care Into Standard Oncology Care Guideline. Most studies refer the SQ to solid tumors and very little is known about the application of this tool in the context of HM. We evaluated the prognostic performance of the SQ among patients with HM. Methods: Prospective cohort study conducted from August to December 2021 through the application of interviews with hematologists from the Federal University of Bahia Clinics Hospital (HUPES-UFBA). Adult patients (age≥18) with HM admitted to the Hematology ward of Clinics Hospital during the study period were included and the SQ was answered by the physicians responsible for the patients at the time of inclusion. Clinical information was extracted from medical records. Results: During the study period, 76 patients were included after exclusion criteria (age<18 and not HM) were applied. Median age was 48.8 years-old (SD 17.2), and most participants were female (58%). Regarding the diagnosis, 38.2% had acute leukemia, 25% had R/R (refractory/relapsed) lymphoma, and 30.3% had R/R multiple myeloma. Forty-nine patients (64.5%) had poor performance status (ECOG 3 or 4), and most patients (57.9%) were receiving disease-modifying therapy. Physicians responded “No, I would not be surprised” (positive, SQ+) for 43 (57%) patients. After 12 months, 35 patients (46%) had died, from which 31 (89%) were in the group SQ - “No”. The sensitivity of the SQ was 88%, and specificity was 72%. The positive predictive value was 72%, negative predictive value was 88%, and accuracy was 79%. Notably, although clinicians found that 43 (57%) patients had a life expectancy of less than 12 months, only 24 (31.6%) were being followed-up with PC at the time of the interview. Conclusion: The SQ has been shown to correctly estimate death among 68.3% of patients with blood cancers in the previous literature. In our study, the SQ demonstrated high sensitivity and potential clinical use. However, even within a center with full availability of a PC team dedicated to patients with advanced diseases and high morbidity and mortality, such as acute leukemia, the hematology team had rarely referred patients to PC. Despite advances on the importance of PC in the care of patients with HM, there is still an important delay when requesting the involvement of this specialty. Thus, effective strategies in order to integrate PC within this scenario are crucial.

Penerapan Penerapan Metode AHP dalam Menentukan Tingkat Kepuasan Pelanggan di Pratama Motor 2

Abstract – AHP is a method for ranking the best decision alternatives, when the decision maker has many objectives or criteria that must be met or consired. This method combines the strengths of the decision maker’s feelings and logic, then synthesizes various considerations into result that match the decision maker’s intuitive estimates as presented in the considerations that have been made. The end result of the AHP process is the priorities of the best criteria in decision making. This study aims to determine the results of decision making in determining the level of customer satisfactions using the Analytical Hierarchy Process (AHP) method. And the testing the calculations in this study using Expert Choice Software. Based on the results of the AHP analysis, it was concluded that sub-criterion D1, namely special attention to each customer, is the top priority for the sub-criteria with a value of 0,452 or 45,2%. And the lowest value is the Responsiveness criterion with a value of 0,156 or 15,6%.
 Keywords — AHP, Analytical Hierarchy Process , Customes Satisfaction,

Predicting 1-Year Mortality in Outpatients With Heart Failure With Reduced Left Ventricular Ejection Fraction: Do Empiric Models Outperform Physician Intuitive Estimates? A Multicenter Cohort Study.

Many studies have demonstrated that physicians often err in estimating patient prognosis. No studies have directly compared physician to model predictive performance in heart failure (HF). We aimed to compare the accuracy of physician versus model predictions of 1-year mortality. This multicenter prospective cohort study on 11 HF clinics in 5 provinces in Canada included consecutive consented outpatients with HF with reduced left ventricular ejection fraction (<40%). By collecting clinical data, we calculated predicted 1-year mortality using the Seattle HF Model (SHFM), the Meta-Analysis Global Group in Chronic HF score, and the HF Meta-Score. HF cardiologists and family doctors, blinded to model predictions, estimated patient 1-year mortality. During 1-year follow-up, we recorded the composite end point of mortality, urgent ventricular assist device implant, or heart transplant. We compared physicians and model discrimination (C statistic), calibration (observed versus predicted event rate), and risk reclassification. The study included 1643 patients with ambulatory HF with a mean age of 65 years, 24% female, and mean left ventricular ejection fraction of 28%. Over 1-year follow-up, 9% had an event. The SHFM had the best discrimination (SHFM C statistic 0.76; HF Meta-Score 0.73; Meta-Analysis Global Group in Chronic Heart Failure 0.70) and calibration. Physicians' discrimination differed little (0.75 for HF cardiologists and 0.73 for family doctors) but both physician groups substantially overestimated risk by >10% in both low- and high-risk patients (poor calibration). In risk reclassification analysis, among patients without events, the SHFM better classified 51% in comparison to HF cardiologists and 43% in comparison to family doctors. In patients with events, the SHFM erroneously assigned lower risk to 44% in comparison to HF cardiologists and 34% in comparison to family doctors. Family doctors and HF cardiologists showed adequate risk discrimination, with however substantial overestimation of absolute risk. Predictive models showed higher accuracy. Incorporating models in family and HF cardiology practices may improve patient care and resource use in HF with reduced left ventricular ejection fraction. URL: https://www. gov; Unique identifier: NCT04009798.

Accuracy of physicians’ intuitive risk estimation in the diagnostic management of pulmonary embolism: an individual patient data meta-analysis

BackgroundIn patients clinically suspected of having pulmonary embolism (PE), physicians often rely on intuitive estimation (“gestalt”) of PE presence. Although shown to be predictive, gestalt is criticized for its assumed variation across physicians and lack of standardization. ObjectivesTo assess the diagnostic accuracy of gestalt in the diagnosis of PE and gain insight into its possible variation. MethodsWe performed an individual patient data meta-analysis including patients suspected of having PE. The primary outcome was diagnostic accuracy of gestalt for the diagnosis of PE, quantified as risk ratio (RR) between gestalt and PE based on 2-stage random-effect log-binomial meta-analysis regression as well as gestalts’ sensitivity and specificity. The variability of these measures was explored across different health care settings, publication period, PE prevalence, patient subgroups (sex, heart failure, chronic lung disease, and items of the Wells score other than gestalt), and age. ResultsWe analyzed 20 770 patients suspected of having PE from 16 original studies. The prevalence of PE in patients with and without a positive gestalt was 28.8% vs 9.1%, respectively. The overall RR was 3.02 (95% CI, 2.35-3.87), and the overall sensitivity and specificity were 74% (95% CI, 68%-79%) and 61% (95% CI, 53%-68%), respectively. Although variation was observed across individual studies (I2, 90.63%), the diagnostic accuracy was consistent across all subgroups and health care settings. ConclusionA positive gestalt was associated with a 3-fold increased risk of PE in suspected patients. Although variation was observed across studies, the RR of gestalt was similar across prespecified subgroups and health care settings, exemplifying its diagnostic value for all patients suspected of having PE.

Job training and firm efficiency: evidence from a meta-frontier analysis

ABSTRACT This study analyzes the effect of vocational training on technology efficiency (TE) using Workplace Panel Survey data from South Korea. We apply both stochastic frontier analysis (SFA) and meta-frontier analysis (MFA) to compare the TEs of firms with vocational training against the counterpart without vocational training. Our findings from the SFA, which assumes a homogeneous level of technology across comparison groups, confirm that firms without vocational training are more efficient. MFA, which is used to compare the relative efficiencies of different skill levels, provides more intuitive estimation results. Namely, companies that conduct vocational training are closer to the meta-frontier – higher efficiency – than those that do not.

An adaptive channel estimation scheme based on redundancy minimization for filtered OFDM networks

Filtered orthogonal frequency division multiplexing (F-OFDM), a technology which is being considered as a promising platform for beyond 4G era is expected to help deliver the new features at millimeter wave in the new 5 th generation of cellular communication. Some of its key features notably better spectral utilization, enhanced throughput and immunity to interference can be enabling for the new cellular standards. These features of filtered OFDM comes with strict requirements of filter design, guard tone managements, and efficient channel state information harness. This paper is intended to propose an intuitive channel estimation scheme which will allow efficient acquisition of channel state information (CSI) through exploiting the redundant steps of the conventional pilot training-based algorithms and by also using an adaptive weight to expedite the minimization of the error between the estimated values and the actual values. Various simulations will follow to demonstrate the superiority of the scheme over traditional pilot-based algorithms and thus prove its utility in the current 5G cellular era.

Learning Post-Stroke Gait Training Strategies by Modeling Patient-Therapist Interaction.

For safe and effective robot-aided gait training, it is essential to incorporate the knowledge and expertise of physical therapists. Toward this goal, we directly learn from physical therapists' demonstrations of manual gait assistance in stroke rehabilitation. Lower-limb kinematics of patients and assistive force applied by therapists to the patient's leg are measured using a wearable sensing system which includes a custom-made force sensing array. The collected data is then used to characterize a therapist's strategies in response to unique gait behaviors found within a patient's gait. Preliminary analysis shows that knee extension and weight-shifting are the most important features that shape a therapist's assistance strategies. These key features are then integrated into a virtual impedance model to predict the therapist's assistive torque. This model benefits from a goal-directed attractor and representative features that allow intuitive characterization and estimation of a therapist's assistance strategies. The resulting model is able to accurately capture high-level therapist behaviors over the course of a full training session (r2=0.92, RMSE=0.23Nm) while still explaining some of the more nuanced behaviors contained in individual strides (r2=0.53, RMSE=0.61Nm). This work provides a new approach to control wearable robotics in the sense of directly encoding the decision-making process of physical therapists into a safe human-robot interaction framework for gait rehabilitation.

Restricted Mean Survival Time Analysis to Estimate SGLT2i–Associated Heterogeneous Treatment Effects on Primary and Secondary Prevention of Cardiorenal Outcomes in Patients With Type 2 Diabetes in Taiwan

Increasing numbers of post hoc analyses have applied restricted mean survival time (RMST) analysis on the aggregated-level data from clinical trials to report treatment effects, but studies that use individual-level claims data are needed to determine the feasibility of RMST analysis for quantifying treatment effects among patients with type 2 diabetes in routine clinical settings. To apply RMST analysis for assessing sodium-glucose cotransporter-2 inhibitor (SGLT2i)-associated cardiovascular (CV) events and estimating heterogenous treatment effects (HTEs) on CV and kidney outcomes in routine clinical settings. This comparative effectiveness study of Taiwan's National Health Insurance Research Database examined 21 144 propensity score (PS)-matched pairs of patients with type 2 diabetes with SGLT2i and dipeptidyl peptidase-4 inhibitor (DPP4i) treatment for assessing CV outcomes, and 19 951 PS-matched pairs of patients with type 2 diabetes with SGLT2i and DPP4i treatment for assessing kidney outcomes. Patients were followed until December 31, 2018. Statistical analysis was performed from August 2021 to April 2022. Newly stable SGLT2i or DPP4i use in 2017. Study outcomes were CV events including hospitalization for heart failure (HHF), 3-point major adverse CV events (3P-MACE: nonfatal myocardial infarction [MI], nonfatal stroke, and CV death), 4-point MACE (4P-MACE: HHF and 3P-MACE), and all-cause death, and chronic kidney disease (CKD). RMST and Cox modeling analyses were applied to estimate treatment effects on study outcomes. After PS matching, the baseline patient characteristics were comparable between 21 144 patients with stable SGLT2i use (eg, mean [SD] age: 58.3 [10.7] years; 11 990 [56.7%] male) and 21 144 patients with stable DPP4i use (eg, mean [SD] age: 58.1 [11.6] years; 12 163 [57.5%] male) for assessing CV outcomes, and those were also comparable between 19 951 patients with stable SGLT2i use (eg, mean [SD] age: 58.1 [10.7] years; 11 231 [56.2%] male) and 19 951 patients with stable DPP4i use (eg, mean [SD] age: 57.9 [11.5] years; 11 340 [56.8%] male) for assessing kidney outcome. The 2-year difference in RMST between patients with SGLT2i use and patients with DPP4i use was 4.99 (95% CI, 3.56-6.42) days for HHF, 4.12 (95% CI, 2.72-5.52) days for 3P-MACE, 7.72 (95% CI, 5.83-9.61) days for 4P-MACE, 1.26 (95% CI, 0.47-2.04) days for MI, 2.70 (95% CI, 1.57-3.82) days for stroke, 0.69 (95% CI, 0.28-1.11) days for CV death, 6.05 (95% CI, 4.89-7.20) days for all-cause death, and 14.75 (95% CI, 12.99-16.52) days for CKD. Directions of hazard ratios from Cox modeling analyses were consistent with RMST estimates. No association was found between study treatment and the negative control outcome (dental visits for tooth care). Consistent results across sensitivity analyses using high-dimensional PS-matched and PS-weighting approaches supported the validity of primary analysis results. Largest difference in RMST of SGLT2i vs DPP4i use for HHF and CKD was found among patients with established heart failure (30.80 [95% CI, 5.08-56.51] days) and retinopathy (40.43 [95% CI, 31.74-49.13] days), respectively. In this comparative effectiveness study, RMST analysis was feasible for translating treatment effects into more clinical intuitive estimates and valuable for quantifying HTEs among diverse patients in routine clinical settings.

Investigating First and Second Language Speaker Intuitions of Phrasal Frequency and Association Strength of Multiword Sequences

AbstractIn this study, we investigated the accuracy of first language (L1) and second language (L2) speakers’ intuitive judgments of phrasal frequency and collocation strength, and examined the linguistic influences that give rise to these judgments. L1 and L2 speakers of English judged 180 adjective–noun collocations as (a) high frequency, medium frequency, or low frequency and (b) high association, medium association, or low association. Results showed that neither L1 nor L2 speakers demonstrated accurate intuitive judgments of phrasal frequency and association strength. Both groups of participants employed linguistic information at phrase and single‐word levels when giving intuitive statistical estimates. We found judgments of phrasal frequency and association strength to be intertwined for both L1 and L2 speakers. Taken together, these findings shed new insight on understanding language users’ statistical knowledge of multiword sequences.

Logical differencing in dyadic network formation models with nontransferable utilities

This paper considers a semiparametric model of dyadic network formation under nontransferable utilities (NTU). Such dyadic links arise frequently in real-world social interactions that require bilateral consent but by their nature induce additive non-separability. In our model we show how unobserved individual heterogeneity in the network formation model can be canceled out without requiring additive separability. The approach uses a new method we call logical differencing. The key idea is to construct an observable event involving the intersection of two mutually exclusive restrictions—derived based on weak multivariate monotonicity—on the fixed effects. Based on this identification strategy we provide consistent estimators of the network formation model under NTU. Finite-sample performance of our method is analyzed in a simulation study, and an empirical illustration using the risk-sharing network data from Nyakatoke demonstrates that our proposed method is able to obtain economically intuitive estimates.

Predicting 1-Year Mortality in Ambulatory Heart Failure Patients: Empiric Models Outperform Physician Intuitive Estimates

<h3>Purpose</h3> This multicenter Canadian prospective cohort study evaluated the accuracy of physician predictions in comparison to model predictions in estimating 1-year mortality in ambulatory patients with heart failure (HF). <h3>Methods</h3> We included consented consecutive ambulatory HF patients (left ventricular ejection fraction (LVEF) <40%) followed at 11 HF clinics in 5 provinces in Canada (2018-2020). In a closed-question survey, HF cardiologists and family doctors, using their clinical judgment, estimated patient 1-year mortality in absolute terms. We collected clinical information to describe the population and calculate predicted mortality using three predictive models: the Seattle HF Model (SHFM), the MAGGIC score and the HF Meta-score. We documented patients' mortality over one year. Physicians were unaware of model predictions. We calculated mortality using Cox proportional hazards and assessed model and physician predictive accuracy using discrimination and calibration. <h3>Results</h3> We included 1,563 ambulatory HF patients, mean age of 63±14 years, 24% female, 70% NTHA class I-II, mean LVEF of 28±7%, and median brain natriuretic peptide of 350 pg/ml (25^th-75^th percentile 130-700). The population 1-year mortality was 7% (95%CI 6%-8%). Models' discrimination (c-statistic 0.80 for MAGGIC and HF meta-score, 0.84 for SHFM) proved superior to physicians (0.77 for family doctors and 0.78 for HF cardiologists). Models' calibration was adequate for patients at lower risk, but overestimated risk in those whose 1-year mortality was > 20%. In contrast, family doctors and HF cardiologists overestimated risk throughout the risk spectrum. In low-risk patients (1-year mortality ∼1%), physicians predicted a risk 7%-10%; in medium-risk patients (1-year mortality ∼5%), physicians predicted 15%-20%; and in high-risk patients (mortality >15%), physicians predicted >25%. <h3>Conclusion</h3> Predictive models showed higher accuracy in predicting 1-year mortality in ambulatory HF population than family doctors and HF cardiologists, both of whom consistently overestimated risk, potentially exposing patients early to more aggressive care. Incorporating predictive models in family and HF cardiology practices may improve patient management and resource use.

Robust, Primitive, and Unsupervised Quality Estimation for Segmentation Ensembles.

A multitude of image-based machine learning segmentation and classification algorithms has recently been proposed, offering diagnostic decision support for the identification and characterization of glioma, Covid-19 and many other diseases. Even though these algorithms often outperform human experts in segmentation tasks, their limited reliability, and in particular the inability to detect failure cases, has hindered translation into clinical practice. To address this major shortcoming, we propose an unsupervised quality estimation method for segmentation ensembles. Our primitive solution examines discord in binary segmentation maps to automatically flag segmentation results that are particularly error-prone and therefore require special assessment by human readers. We validate our method both on segmentation of brain glioma in multi-modal magnetic resonance - and of lung lesions in computer tomography images. Additionally, our method provides an adaptive prioritization mechanism to maximize efficacy in use of human expert time by enabling radiologists to focus on the most difficult, yet important cases while maintaining full diagnostic autonomy. Our method offers an intuitive and reliable uncertainty estimation from segmentation ensembles and thereby closes an important gap toward successful translation of automatic segmentation into clinical routine.

Estimating the correlation in network disturbance models

AbstractThe network disturbance model of P. Doreian (1989), expresses the dependency between observations taken at the vertices of a network by modelling the correlation between neighbouring vertices, using a single correlation parameter $\rho$. It has been observed that estimation of $\rho$ in dense graphs, using the method of maximum likelihood, leads to results that can be both biased and very unstable. In this article, we sketch why this is the case, showing that the variability cannot be avoided, no matter how large the network. We also propose a more intuitive estimator of $\rho$, which shows little bias. The related network effects model is briefly discussed.

Private Portfolio Attribution Analysis

There are many methods for conducting performance attribution with portfolios containing only liquid assets. A lack of periodic asset return data and a clear definition of what constitutes an appropriate market benchmark thwarts efforts to perform similar types of attribution analyses for portfolios of private equity funds (and other illiquid investments). In this article, the authors propose a method for decomposing private fund portfolio performance into effects from timing, strategy selection, geographic focus, sizing of fund allocation, and fund selection attributes. They test the method with a simulation study and derive approximate confidence intervals for assessing attribute selection skill using a large historical dataset of buyout and venture capital funds. <b>Key Findings</b> ▪ The method provided for evaluating the value created from decisions about the timing, size, geography, and asset class of private fund commitments in this article also provides a residual component. Analysts and investors can interpret this component as value-added by fund selection. ▪ The method provides intuitive estimates in terms of contributions to return multiples (MOICs) and IRRs. ▪ The authors conduct a simulation study using a large sample of private equity buyout and venture capital funds to estimate approximate confidence intervals for each allocation characteristic. Practitioners can use these intervals to statistically separate skill from luck.