General Likelihood Research Articles

Considerations for Measuring Caregiver Talk in Interactions With Infants at Elevated and Population-Level Likelihood for Autism: Deriving Stable Estimates.

This study aims to help researchers design observational measurement systems that yield sufficiently stable scores for estimating caregiver talk among caregivers of infant siblings of autistic and non-autistic children. Stable estimates minimize error introduced by facets of the measurement system, such as variability between coders or measurement sessions. Analyses of variance were used to partition error variance between coder and session and to derive g coefficients. Decision studies determined the number of sessions and coders over which scores must be averaged to achieve sufficiently stable g coefficients (0.80). Twelve infants at elevated likelihood of an autism diagnosis and 12 infants with population-level likelihood of autism diagnosis participated in two semistructured observation sessions when the children were 12-18 months of age and again 9 months later. Caregiver follow-in talk was coded from these sessions. Two sessions and one coder were needed to achieve sufficient stability for follow-in talk and follow-in comments for both groups of infants at both time points. However, follow-in directives did not reach sufficient stability for any combination of sessions or coders for the population-level likelihood group at either time point, or for the elevated likelihood group at Time 2. Researchers should plan to collect at least two sessions to derive sufficiently stable estimates of caregiver talk in infants at elevated and general population-level likelihood for autism. https://doi.org/10.23641/asha.27996875.

A water quality prediction approach for the Downstream and Delta of Dongjiang River Basin under the joint effects of water intakes, pollution sources, and climate change

Water quality prediction is an important means for scientific water environment management and early warning measures. However, its reliability is often restricted by the uncertainties of parameters and pollution sources. Previous methods for resolving these uncertainties still have shortcomings due to low computational efficiency and incomplete coverage of mechanisms. With insufficient data, they also overlooked revealing the comprehensive impact of water intakes, pollution sources, and climate change on water quality in river networks. Insight understanding migration law and the main driving mechanisms under these factors is crucial for water resources and sustainable socioeconomic development. Here, we developed an efficient multi-scenario ensemble water quality prediction (MEWQP) approach to address the deficiency. The Downstream and Delta of Dongjiang River Basin was the demonstrative case. Firstly, we developed multiple parameters and pollution sources inverse estimation method by incorporating the Water Quality Analysis Simulation Program (WASP), general likelihood uncertainty estimation, and inverse methods to improve accuracy. Secondly, the Markov-Chain was used to predict flows affected by climate change. Thirdly, 60 scenarios were analyzed to quantify the impacts of multiple factors on water quality including 5-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), and dissolved oxygen (DO), and identify an optimized scheme of water intakes. Results show that: (1) WASP has good performance for BOD5, NH3-N, and DO with Mean Absolute Relative Error of 5.87%, 9.19%, and 15.25%, respectively; (2) pollution sources have the greatest impact, followed by extreme climate and intakes. Their combined impacts are greater than individuals; (3) under non-water intakes DO can be improved by 31%, also offset the impact of climate change by 16%; (4) the optimized scheme of intakes could improve DO by 6%. Therefore, decision-makers should simultaneously adjust water intake and reduce pollution sources to obtain high-quality water resources.

To whom do people reply in comment sections? Effects of attitude (un)congeniality, age, confidence, and knowledge

Phenomena like echo chambers and societal polarization have often been linked to an individual preference for like-minded information (selective exposure). This view has been challenged recently: behavior on comment sections in online forums suggests the opposite dynamic, with users more likely to reply to attitudinally uncongenial content. Three experimental studies (total N = 1524) explore boundary conditions of this uncongeniality bias by measuring participants’ tendency to reply to comments on climate science. Studies 1 (student sample) and 2 (non-student sample) replicate the uncongeniality bias. However, Study 3 (representative for age and gender in Germany) yielded a surprising congeniality bias (a preference for replying to congenial comments) which was driven by participants with higher age. Further results across studies suggest that high confidence increases engagement (general likelihood to reply) but reduces antagonism (likelihood to reply to uncongenial content). Conversely, high knowledge reduces engagement but increases the uncongeniality bias.

Pathways to professional mental care in the Swiss young adult community: a case-control study.

Treatment success for mental health (MH) problems depends, among others, on the timeliness of help-seeking. Therefore, we studied the effect of symptoms and reasons for help-seeking on the point-of-contact and the most intensive professional treatment in a community sample. Participants were recruited as part of the 'Bern Epidemiological At-Risk' (BEAR) study on 16-40-year-old community persons of the Swiss canton Bern. Of the 2,683 participants, 615 (22.9%) reported at least one instance of help-seeking for MH problems and were selected for the presented analyses. Help-seeking behavior was assessed by a modified version of the 'WHO pathway-to-care questionnaire', from which the outcome 'most intensive MH professional contact' was generated. The effect of symptoms and reasons for help-seeking were analyzed in separate models using path analyses. Most help-seeking persons sought MH professional help (n = 405; 65.9%) with a high number of medical pre-contacts (n = 233; 37.9%). The 'most intensive MH professional contact' was provided after an average of 1.47 contacts. Both models showed negative associations between non-MH professional pre-contacts and the most intensive, likely most adequate MH treatment. In the symptom model, 'substance misuse' and 'central-vegetative problems' increased the general likelihood of MH professional contact. Our findings highlight the importance of the first point-of-contact in pathways to adequate MH care and, when seeking help from non-MH professional, of quick referrals to MH professionals. Awareness campaigns or training of health professionals, such as general practitioners, may support timely contact with MH professionals to improve diagnosis, prognosis, and outcome.

Factors Associated with Planned Future Use of PrEP in the Next 3 Months and Likelihood to Use PrEP Among Black Cisgender HIV-negative Women in Texas.

Identifying and then addressing barriers and leveraging facilitators is important to help increase pre-exposure prophylaxis (PrEP) use among Black women vulnerable to HIV acquisition. The present cross-sectional study examined what factors were associated with future plans to use PrEP, and general likelihood to use it among a convenience sample of 152 adult, Black cisgender women from three metropolitan areas in Texas. The final multivariable logistic regression model revealed that relationship status (aOR = 0.20, 95% CI: 0.05-0.73, p < 0.05), PrEP anticipated stigma (aOR = 0.29, 95% CI: 0.10-0.78, p < 0.05), perceived discrimination (aOR = 0.40, 95% CI: 0.21-0.78, p < 0.01) and interest in learning more about PrEP (aOR = 5.32, 95% CI: 2.60-10.9, p < 0.001) were associated with future plans to use PrEP. The final multivariable linear regression model with maximum likelihood estimation identified that perceived discrimination (β=-0.24, SE: -0.38 - -0.10, p < 0.01), perceived HIV risk (β = 0.33, SE: 0.18-0.49, p < 0.001), willingness to use PrEP with condoms (β = 1.26, SE: 0.94-1.60, p < 0.001), and comfort communicating about PrEP with a provider (β = 0.23, SE: 0.06-0.41, p < 0.01) were associated with general likelihood to use PrEP. Findings reveal key factors that warrant further attention and examination toward improving PrEP use within this population.

Purely subjective revealed ambiguity

The objective of this paper is to show how ambiguity, and an individual’s response to it, can be modeled formally in the context of a likelihood relation over events. When certain structural conditions are met, we introduce the rational core of a likelihood relation and show that it can be represented by a set of priors, as suggested by Ghirardato et al. (2004). We also prove that a general likelihood relation can be represented in the spirit of Hurwicz’s “optimism index” model (Hurwicz, 1951). We then obtain a fully subjective model of likelihood judgments under ambiguity for ambiguity averse individuals.

Is Everyone Probably Elsewhere?

It has been widely discussed whether the existence of other universes than the known universe is a purely metaphysical consideration. This paper argues to the contrary that the existence of other universes is a meaningful hypothesis, which can be tested based on observable data. The paper applies the perspective of observer bias as an approach for assigning probabilities to the mutually excluding hypotheses of universe vs. multiverse, i.e., whether the known universe is the only universe, or just one universe in an ensemble of universes, a so-called multiverse. The basic idea of the paper relies on the following consideration. In a multiverse, the vast majority of observers would live in universes that are more life friendly. Thus, conditional on there being a multiverse, we should expect to find ourselves in a universe with values of the fundamental parameters that provide particularly fertile grounds for life. In contrast, if there is only a single universe, it may well be the case that there is still a few observers even if the parameters are far from ideal for life, and in that case, those observers will find values of the parameters that are not ideal for intelligent life. It may well be the case that, among all parameter configurations that allow life at least somewhere, those that are not ideal for life far outnumber those that are ideal for life. Based on this elementary consideration, the paper proceeds to propose a quantitative framework to determine probabilities for either hypothesis. In particular, it is described how a future ab initio determination of some of the factors in the Drake equation may be used to infer which one of the two hypotheses is the more likely. A quantitative approach to this end is proposed. Expressing these factors of the Drake equation in the context of the two hypotheses, a general likelihood approach is first described. Then, to support intuition, example computations are provided, illustrating how an actual hypothesis test would work in practice.

General Jackknife empirical likelihood and its applications

General Jackknife empirical likelihood and its applications

Improving estimation efficiency for two-phase, outcome-dependent sampling studies

Two-phase outcome dependent sampling (ODS) is widely used in many fields, especially when certain covariates are expensive and/or difficult to measure. For two-phase ODS, the conditional maximum likelihood (CML) method is very attractive because it can handle zero Phase 2 selection probabilities and avoids modeling the covariate distribution. However, most existing CML-based methods use only the Phase 2 sample and thus may be less efficient than other methods. We propose a general empirical likelihood method that uses CML augmented with additional information in the whole Phase 1 sample to improve estimation efficiency. The proposed method maintains the ability to handle zero selection probabilities and avoids modeling the covariate distribution, but can lead to substantial efficiency gains over CML in the inexpensive covariates, or in the influential covariate when a surrogate is available, because of an effective use of the Phase 1 data. Simulations and a real data illustration using NHANES data are presented.

A Novel Adaptive Zero Velocity Detection Algorithm Based on Improved General Likelihood Ratio Test Detector

The zero-velocity update (ZUPT) method is an effective technique, which has been widely used in low-cost foot-mounted inertial navigation systems. It can mitigate the accumulated error by using the fact that, when the foot is stationary, the velocity should be zero. However, it is hard to detect the zero-velocity phases (ZVPs) correctly in various motion modes. To deal with this problem, a novel adaptive zero velocity detection algorithm based on an improved general likelihood ratio test (IGLRT) detector is proposed. The prior knowledge of the proposed IGLRT detector is the minima output value of inertial sensors, which is determined by an improved peak-valley detection method, while the prior knowledge of the traditional general likelihood ratio test (GLRT) method is based on the hypothesis that, when the foot is stationary, the magnitude of angular rates is zero, and the magnitude of accelerometers is equal to the magnitude of gravitation. Also, an adaptive method is proposed to enhance the robustness of the IGLRT, where the thresholds of IGLRT can be adjusted adaptively according to the amplitude of accelerometers. Field tests show that the proposed algorithm performs better than conventional and state-of-the-art approaches in mixed motion modes.

Longitudinal Relations Between Early Sensory Responsiveness and Later Communication in Infants with Autistic and Non-autistic Siblings.

Early differences in sensory responsiveness may contribute to difficulties with communication among autistic children; however, this theory has not been longitudinally assessed in infants at increased familial versus general population-level likelihood for autism (Sibs-autism vs. Sibs-NA) using a comprehensive battery of sensory responsiveness and communication. In a sample of 40 infants (20 Sibs-autism, of whom six were later diagnosed with autism; 20 Sibs-NA), we tested (a) associations between sensory responsiveness at 12-18months and communication 9months later and (b) evaluated whether such associations were moderated by sibling group, autism diagnosis, or age. We found negative zero-order correlations between sensory responsiveness (i.e., caregiver reported hyperresponsiveness and hyporesponsiveness; an observational measure of hyperresponsiveness) and later communication. Additionally, caregiver reported sensory seeking was negatively associated with later expressive communication only in Sibs-NA. Limitations include our relatively small sample size of infants diagnosed with autism. Implications for future research are discussed.

Conditional interval reduction method: A possible new direction for the optimization of process based models

Application of process-based models at different spatial scales requires their proper parameterization. This task is typically executed using trial-and-error parameter adjustment or a probabilistic method. Practical application of the probabilistic methods is hampered by methodological complexity and lack of interpretability. Here we present a novel approach for the parameterization of process-based models that we call as conditional interval refinement method (CIRM). The method can be best described as the combination of a probabilistic approach and the advantages of the expert-based parameter adjustment. CIRM was demonstrated by optimizing the Biome-BGCMuSo biogeochemical model using maize yield observations. The proposed approach uses the General Likelihood Uncertainty Estimation (GLUE) method with additional expert knowledge, supplemented by the construction and interpretation of decision trees. It was demonstrated that the iterative, fully automatic method successfully constrained the parameter intervals meanwhile our confidence on the parameters increased. The algorithm can easily be implemented with other process-based models.

A unifying framework for continuous tumour growth modelling of breast cancer screening data

The aim of the current article is to present theory that can help unify continuous growth approaches for modelling breast cancer tumour growth based on human data. We present a framework that has three main features: a general likelihood function to account for patient specific screening regiments; stable disease assumptions describing tumour population dynamics; and mathematical models describing tumour growth, individual variation in tumour growth, a hazard for symptomatic detection, and screening test sensitivity. The framework is able to incorporate any random effects distributions for the tumour growth rate parameter, any hazard functions for symptomatic tumour detection, as well as any monotonously increasing function for the tumour growth model.Based on a sample of 1902 incident breast cancer cases with data on mammography screening, we show how the framework can be used to estimate tumour growth based on different growth functions.

Primary School Children's Self-Reports of Attention Deficit Hyperactivity Disorder-Related Symptoms and Their Associations With Subjective and Objective Measures of Attention Deficit Hyperactivity Disorder.

BackgroundThe diagnosis of Attention deficit hyperactivity disorder (ADHD) is primarily dependent on parents’ and teachers’ reports, while children’s own perspectives on their difficulties and strengths are often overlooked.GoalTo further increase our insight into children’s ability to reliably report about their ADHD-related symptoms, the current study examined the associations between children’s self-reports, parents’ and teachers’ reports, and standardized continuous performance test (CPT) data. We also examined whether the addition of children’s perceptions of ADHD-symptoms to parents’ and teachers’ reports would be reflected by objective and standardized data.MethodsThe study included 190 children with ADHD, aged 7–10 years, who were referred to a pediatric neurologic clinic. A retrospective analysis was conducted using records of a clinical database. Obtained data included children’s self-reports of their attention level and ADHD-related symptoms, parent, and teacher forms of the Conners ADHD rating scales, Child Behavior Checklist (CBCL), Teacher’s Report Form (TRF), and CPT scores.ResultsChildren’s self-evaluations of their functioning were globally associated with their teachers’ and parents’ evaluations, but not uniquely. Children’s self-reports of ADHD symptoms were not uniquely linked to a specific CPT impairment index, but to a general likelihood of having an impaired CPT. The CPT performance successfully distinguished between the group of children who defined themselves as inattentive and those who did not.ConclusionPrimary school children with ADHD are able to identify their limitations and needs difficulties and that their perspectives should inform clinical practice and research. The clinical and ethical imperative of taking children’s perspectives into account during ADHD diagnosis and treatment is highlighted.

Source-anchored, trace-anchored, and general match score-based likelihood ratios for camera device identification.

Forensic camera device identification addresses the scenario, where an investigator has two pieces of evidence: a digital image from an unknown camera involved in a crime, such as child pornography, and a person of interest’s (POI’s) camera. The investigator wants to determine whether the image was taken by the POI’s camera. Small manufacturing imperfections in the photodiode cause slight variations among pixels in the camera sensor array. These spatial variations, called photo‐response non‐uniformity (PRNU), provide an identifying characteristic, or fingerprint, of the camera. Most work in camera device identification leverages the PRNU of the questioned image and the POI’s camera to make a yes‐or‐no decision. As in other areas of forensics, there is a need to introduce statistical and probabilistic methods that quantify the strength of evidence in favor of the decision. Score‐based likelihood ratios (SLRs) have been proposed in the forensics community to do just that. Several types of SLRs have been studied individually for camera device identification. We introduce a framework for calculating and comparing the performance of three types of SLRs — source‐anchored, trace‐anchored, and general match. We employ PRNU estimates as camera fingerprints and use correlation distance as a similarity score. Three types of SLRs are calculated for 48 camera devices from four image databases: ALASKA; BOSSbase; Dresden; and StegoAppDB. Experiments show that the trace‐anchored SLRs perform the best of these three SLR types on the dataset and the general match SLRs perform the worst.

Mechanisms by which Early Eye Gaze to the Mouth During Multisensory Speech Influences Expressive Communication Development in Infant Siblings of Children with and without Autism.

Looking to the mouth of a talker early in life predicts expressive communication. We hypothesized that looking at a talker's mouth may signal that infants are ready for increased supported joint engagement and that it subsequently facilitates prelinguistic vocal development and translates to broader gains in expressive communication. We tested this hypothesis in 50 infants aged 6-18 months with heightened and general population-level likelihood of autism diagnosis (Sibs-autism and Sibs-NA; respectively). We measured infants' gaze to a speaker's face using an eye tracking task, supported joint engagement during parent-child free play sessions, vocal complexity during a communication sample, and broader expressive communication. Looking at the mouth was indirectly associated with expressive communication via increased higher-order supported joint engagement and vocal complexity. This indirect effect did not vary according to sibling status. This study provides preliminary insights into the mechanisms by which looking at the mouth may influence expressive communication development.

Standardized exponentiated Gumbel error innovation distribution in modelling volatility models

Here we proposed three classes of volatility models using Standardized Exponentiated Gumbel Error Innovation Distribution (SEGEID). Important statistical and mathematical properties of this models have been discussed and derived. Hence, the parameters of the volatility models are discussed using the general log likelihood function of the SEGEID. Finally, the volatility models were obtained through partial derivative by adopting a method of numerical method BFGS.

In Search of a Soil Moisture Content Simulation Model: Mechanistic and Data Mining Approach Based on TDR Method Results.

Soil moisture content simulation models have continuously been an important research objective. In particular, the comparisons of the performance of different model types deserve proper attention. Therefore, the quality of selected physically-based and statistical models was analyzed utilizing the data from the Time Domain Reflectometry technique. An E-Test measurement system was applied with the reflectogram interpreted into soil volumetric moisture content by proper calibration equations. The gathered data facilitated to calibrate the physical model of Deardorff and establish parameters of: support vector machines, multivariate adaptive regression spline, and boosted trees model. The general likelihood uncertainty estimation revealed the sensitivity of individual model parameters. As it was assumed, a simple structure of statistical models was achieved but no direct physical interpretation of their parameters, contrary to a physically-based method. The TDR technique proved useful for the calibration of different soil moisture models and a satisfactory quality for their future exploitation.

ALPF-GLRT based fault detection method for small faults applied to redundant IMUs

Since small faults exhibit a very close magnitude to sensor noises, the probability of missing detection (PMD) in existing methods will increase sharply in the presence of small faults. To address such a problem, this paper proposes a novel fault detection method for small faults applied to redundant IMUs. First, this method introduces an adaptive low-pass filter (ALPF) into the general likelihood ratio test (GLRT) by filtering the parity residuals of the GLRT model, thereby reducing the disturbance of sensor noises on fault detection. Subsequently, since the introduction of ALPF leads to the changes of the parity residual statistics, the covariance of parity residual should be recalculated at the respective sample instant. Lastly, to theoretically prove the superiority of the proposed method for small faults, the minimum detection bias (MDB) is derived and calculated, thereby validating that the MDB of the proposed method is lower than that of the conventional GLRT method. As indicated from the simulation results, the PMD of the proposed method decreases significantly for small faults compared with the GLRT method and the Monte Carlo PMD of the proposed method is 0.1% under the fault with the magnitude of 1 sigma, which demonstrates the effectiveness of the proposed method.

P84. Which components of the global alignment proportionality score have the greatest impact on outcomes in adult spinal deformity corrective surgery?

P84. Which components of the global alignment proportionality score have the greatest impact on outcomes in adult spinal deformity corrective surgery?