This paper outlines the three-phase construction of the Startle and Surprise Inventories (Startle-I; Surprise-I) and Visual Analogue Scales for Startle and Surprise (Startle-VAS; Surprise-VAS). In Phase 1, seven experts in the field assessed the content validity of 14 items for surprise, 7 items for startle derived from fundamental and applied literature. Elimination of items was based on a 50% agreement of relevance. In Phase 2, 81 participants completed the retained 19 items nine times, each time immediately after watching a video clip. A multilevel exploratory factor analysis was applied to assess the construct validity of items. In Phase 3, concurrent validity of the Startle-VAS and Surprise-VAS was tested by comparing with the Startle-I and Surprise-I scores, respectively. The first two phases yielded a 11-item two-factor solution, corresponding to the constructs of startle and surprise. These results supported Startle-I and Surprise-I as measures of self-report startle and surprise, with Startle-VAS and Surprise-VAS as efficient alternatives.

Elliptical distributions are a simple and flexible class of distributions that depend on a one-dimensional function, called the density generator. In this chapter, we study the nonparametric estimator of this generator that was introduced by Liebscher (J Multivariate Anal 92(1):205–225, 2005). This estimator depends on two tuning parameters: a bandwidth h—as usual in kernel smoothing—and an additional parameter a that control the behavior near the center of the distribution. We give an explicit expression for the asymptotic MSE at a point x and derive explicit expressions for the optimal tuning parameters h and a. The estimation of the derivatives of the generator is also discussed. A simulation study shows the performance of the new methods.

Background: Robotic devices have shown promise in supporting motor (re)learning. However, there is a limited understanding of how personality traits influence the effectiveness of robot-aided training strategies. Methods: We conducted a motor learning experiment with 40 unimpaired participants who trained to control a virtual pendulum using a robotic haptic device. Before the experiment, we assessed personality traits including the perceived control over life events (Locus of Control), the tendency to turn challenges into engaging activities (Transform of Challenge), and other subscales from Autotelic and Hexad gaming style questionnaires. Participants were divided into two groups, one receiving haptic guidance during training and a second one without assistance. Short- and long-term retention was assessed, and relationships between personality traits, performance metrics, and human-robot interaction metrics were analyzed. Results: Participants with high Transform of Challenge or external Locus of Control characteristics who received physical guidance during training reduced the human-robot interaction forces to a lesser extent compared to the ones who did not receive guidance. Additionally, participants with a high Free Spirit gaming style showed greater sensitivity to how their perception of the guidance affected their performance during the retention phases. Conclusion: Our findings suggest that autotelic personality, Locus of Control, and gaming style modulate motor learning outcomes during robotic-assisted training, affecting both performance and human-robot interaction metrics. This highlights the potential of integrating personality-based adaptations in robot-aided rehabilitation protocols to enhance performance and motor (re)learning. Future works should explore the relationship between personality traits and psychological states (e.g., perceived difficulty, attention) across diverse tasks and guidance methods in clinical populations.

U-statistics constitute a large class of estimators, generalizing the empirical mean of a random variable (Formula presented.) to sums over every (Formula presented.) -tuple of distinct observations of (Formula presented.). They may be used to estimate a regular functional (Formula presented.) of the law of (Formula presented.). When a vector of covariates (Formula presented.) is available, a conditional U-statistic describes the effect of (Formula presented.) on the conditional law of (Formula presented.) given (Formula presented.), by estimating a regular conditional functional (Formula presented.). We state nonasymptotic bounds of general conditional U-statistics and study their asymptotics too. Assuming a parametric model of the conditional functional of interest, we propose a regression-type estimator based on conditional U-statistics. Its theoretical properties are derived, first in a nonasymptotic framework and then in two different asymptotic regimes. Some examples are given to illustrate our methods.

The provision of robotic assistance during motor training has proven to be effective in enhancing motor learning in some healthy trainee groups as well as patients. Personalizing such robotic assistance can help further improve motor (re)learning outcomes and cater better to the trainee's needs and desires. However, the development of personalized haptic assistance is hindered by the lack of understanding of the link between the trainee's personality and the effects of haptic guidance during human-robot interaction. To address this gap, we ran an experiment with 40 healthy participants who trained with a robotic device to control a virtual pendulum to hit incoming targets, either with or without haptic guidance. We found that certain personal traits affected how users adapt and interact with the guidance during training. In particular, those participants with an 'Achiever gaming style' performed better and applied lower interaction forces to the robotic device than the average participant as the training progressed. Conversely, participants with the 'Free spirit game style' increased the interaction force in the course of training. We also found an interaction between some personal characteristics and haptic guidance. Specifically, participants with a higher 'Transformation of challenge' trait exhibited poorer performance during training while receiving haptic guidance compared to an average participant receiving haptic guidance. Furthermore, individuals with an external Locus of Control tended to increase their interaction force with the device, deviating from the pattern observed in an average participant under the same guidance. These findings suggest that individual characteristics may play a crucial role in the effectiveness of haptic guidance training strategies.

In nonparametric statistics, rate-optimal estimators typically balance bias and stochastic error. The recent work on overparametrization raises the question whether rate-optimal estimators exist that do not obey this trade-off. In this work we consider pointwise estimation in the Gaussian white noise model with regression function f in a class of β-Hölder smooth functions. Let ’worst-case’ refer to the supremum over all functions f in the Hölder class. It is shown that any estimator with worst-case bias ≲n^{−β/(2β+1)}≕ψ_n must necessarily also have a worst-case mean absolute deviation that is lower bounded by ≳ψ_n. To derive the result, we establish abstract inequalities relating the change of expectation for two probability measures to the mean absolute deviation.

We propose a new concept of codivergence, which quantifies the similarity between two probability measures P₁,P₂ relative to a reference probability measure P₀. In the neighborhood of the reference measure P₀, a codivergence behaves like an inner product between the measures P₁-P₀ and P₂-P₀. Codivergences of covariance-type and correlation-type are introduced and studied with a focus on two specific correlation-type codivergences, the χ²-codivergence and the Hellinger codivergence. We derive explicit expressions for several common parametric families of probability distributions. For a codivergence, we introduce moreover the divergence matrix as an analogue of the Gram matrix. It is shown that the χ²-divergence matrix satisfies a data-processing inequality.

Background: Solid evidence of the safety and effectiveness of retinoblastoma (RB) conservative treatment using thermotherapy and systemic chemotherapy with long-term follow-up is scarce, especially in low-resource countries. Aims: This study examined the outcomes of this treatment and associated predictors in Vietnam to strengthen the current RB treatment protocol focusing on preserving eye and vision in low-resource settings. Methods and results: A prospective cohort study was conducted at Ho Chi Minh City Eye Hospital in Vietnam from 2005 to 2019. All eligible patients with bilateral RB (one eye already removed and another eye classified as group A or B) and without previous treatment were recruited. All patients received thermotherapy and six cycles of systemic three-agent chemotherapy repeated every 4 weeks. A standardized questionnaire was used to collect information on study participants' age, symptoms, tumor characteristics, treatment, and outcomes. Among 50 eyes of all 50 patients with a median age of 9 (4–20) months, 34 eyes were in group B (68%). The median follow-up time was 60 (60–84) months. All 139 preserved tumors regressed mostly to type 4 (70.4%) and type 3 (23.7%) scars. Kaplan–Meier analysis found the overall globe-salvage rate at 5 years of 91.9% (95% CI: 80.1%–97.7%). Most eyes (41/50, 82%, 95% CI: 69.2%–90.2%) had a final visual acuity ≥0.1. The visual acuity is higher when tumors regressed to a type 4 scar (p =.007, AOR = 8.098, 95% CI: 1.79–36.53) which also shows less enucleation than a type 3 scar (p =.002, AOR = 0.06, 95% CI: 0.01–0.37%). Gender effect on visual acuity after treatment was significant and may be due to discrimination. No major complications were recorded. Conclusion: Conservative treatment of early-stage RB is safe and effective. Long-term, thorough follow-ups of patients post-treatment are needed. The regression patterns of scars could be a useful indicator of treatment failure.

It is a common phenomenon that for high-dimensional and nonparametric statistical models, rate-optimal estimators balance squared bias and variance. Although this balancing is widely observed, little is known whether methods exist that could avoid the trade-off between bias and variance. We propose a general strategy to obtain lower bounds on the variance of any estimator with bias smaller than a prespecified bound. This shows to which extent the bias-variance trade-off is unavoidable and allows to quantify the loss of performance for methods that do not obey it. The approach is based on a number of abstract lower bounds for the variance involving the change of expectation with respect to different probability measures as well as information measures such as the Kullback-Leibler or χ²-divergence. Some of these inequalities rely on a new concept of information matrices. In a second part of the article, the abstract lower bounds are applied to several statistical models including the Gaussian white noise model, a boundary estimation problem, the Gaussian sequence model and the high-dimensional linear regression model. For these specific statistical applications, different types of bias-variance trade-offs occur that vary considerably in their strength. For the trade-off between integrated squared bias and integrated variance in the Gaussian white noise model, we propose to combine the general strategy for lower bounds with a reduction technique. This allows us to reduce the original problem to a lower bound on the bias-variance trade-off for estimators with additional symmetry properties in a simpler statistical model. In the Gaussian sequence model, different phase transitions of the bias-variance trade-off occur. Although there is a non-trivial interplay between bias and variance, the rate of the squared bias and the variance do not have to be balanced in order to achieve the minimax estimation rate.

This article deals with robust inference for parametric copula models. Estimation using canonical maximum likelihood might be unstable, especially in the presence of outliers. We propose to use a procedure based on the maximum mean discrepancy (MMD) principle. We derive nonasymptotic oracle inequalities, consistency and asymptotic normality of this new estimator. In particular, the oracle inequality holds without any assumption on the copula family, and can be applied in the presence of outliers or under misspecification. Moreover, in our MMD framework, the statistical inference of copula models for which there exists no density with respect to the Lebesgue measure on (Formula presented.), as the Marshall-Olkin copula, becomes feasible. A simulation study shows the robustness of our new procedures, especially compared to pseudo-maximum likelihood estimation. An R package implementing the MMD estimator for copula models is available. Supplementary materials for this article are available online.

Purpose:It remains unclear whether preoperative central graft thickness (CGT) contributes to visual outcomes of Descemet stripping automated endothelial keratoplasty (DSAEK). This retrospective cohort study examined the ability of preoperative and postoperative CGT to predict 12-month best spectacle-corrected visual acuity (BSCVA) after DSAEK for Fuchs endothelial corneal dystrophy/moderate pseudophakic bullous keratopathy/second graft.Methods:All consecutive patients who underwent DSAEK in 2015 to 2018 were included. The primary end point was 12-month BSCVA. DSAEK-CGT was measured preoperatively and 6 times between postoperative day 8 and month 12. Eyes were divided according to preoperative CGT 130 m (ultrathin-DSAEK threshold) or 6-month postoperative CGT 100 m (mean 6-month postoperative DSAEK-CGT). The t test assessed CGT evolution of the 4 groups over time. Multivariate analyses examined whether preoperative CGT or 6-month CGT categories predicted 12-month BSCVA. Multivariate analysis assessed the preoperative/perioperative factors that predicted 6-month CGT.Results:A total of 108 eyes (68 patients) underwent DSAEK. Preoperative CGT was >130 and ≤130 m in 87 and 21 eyes, respectively. Postoperative CGT was >100 and ≤100 m in 50 and 58 eyes, respectively. Thin 6-month postoperative grafts thinned significantly more relative to preoperative thickness than thick grafts (P < 0.001). Preoperative CGT subgroups did not show this difference. Six-month postoperative CGT (P = 0.01), but not preoperative CGT, predicted 12-month BSCVA. Preoperative CGT strongly predicted 6-month CGT (P = 0.0003).Conclusions:Postoperative, but not preoperative, DSAEK-CGT predicted 6-month BSCVA. The correlation between preoperative and postoperative CGT and interstudy variation in preoperative CGT measurement accuracy may explain literature disparities regarding the importance of preoperative CGT in DSAEK outcomes.

Meta-elliptical copulas are often proposed to model dependence between the components of a random vector. They are specified by a correlation matrix and a map g, called density generator. While the latter correlation matrix can easily be estimated from pseudo-samples of observations, the density generator is harder to estimate, especially when it does not belong to a parametric family. We give sufficient conditions to non-parametrically identify this generator. Several nonparametric estimators of g are then proposed, by M-estimation, simulation-based inference, or by an iterative procedure available in the R package ElliptCopulas. Some simulations illustrate the relevance of the latter method.