Maximum Likelihood Estimation Of Models Research Articles

Harmonization of Longitudinal Diffusion Tensor Imaging Data of the Pediatric Cervical and Thoracic Spinal Cord at 3T Using Longitudinal ComBat.

Diffusion tensor imaging (DTI) of the spinal cord has been extensively used to identify biomarkers for spinal cord pathology. Previously, the longitudinal ComBat (longComBat) technique was examined to reduce scanner effects in multi-site, multi-scanner spinal cord DTI data. This study aimed to assess its effectiveness on longitudinal scans using a single-scanner pediatric dataset, including healthy and spinal cord injury (SCI) subjects. Two identical datasets were collected from 42 healthy and 27 SCI subjects with a 2-hour interval between scans on a 3T Siemens MRI scanner. Axial DTI images of the entire cervical and thoracic spinal cord were obtained, and various average diffusion tensor metrics (FA, MD, RD, & AD) were measured at each vertebral level. Pearson correlation and intraclass correlation coefficients were used to evaluate inter- and intra-subject agreement pre- and post-harmonization. Minimal improvement in agreement was observed with the mean square residual (MSR) model, while the restricted maximum likelihood estimator (REML) model showed reduced intra-subject agreement in all the tensor metrics. The significant variability between longitudinal DTI scans within a single scanner was likely due to physiological motion rather than scanner effects. Post-harmonization using the longComBat MSR model showed limited improvement in agreement.

Visuotactile integration in individuals with fibromyalgia.

Our brain constantly integrates afferent information, such as visual and tactile information, to perceive the world around us. According to the maximum-likelihood estimation (MLE) model, imprecise information will be weighted less than precise, making the multisensory percept as precise as possible. Individuals with fibromyalgia (FM), a chronic pain syndrome, show alterations in the integration of tactile information. This could lead to a decrease in their weight in a multisensory percept or a general disruption of multisensory integration, making it less beneficial. To assess multisensory integration, 15 participants with FM and 18 pain-free controls performed a temporal-order judgment task in which they received pairs of sequential visual, tactile (unisensory conditions), or visuotactile (multisensory condition) stimulations on the index and the thumb of the non-dominant hand and had to determine which finger was stimulated first. The task enabled us to measure the precision and accuracy of the percept in each condition. Results indicate an increase in precision in the visuotactile condition compared to the unimodal conditions in controls only, although we found no intergroup differences. The observed visuotactile precision was correlated to the precision predicted by the MLE model in both groups, suggesting an optimal integration. Finally, the weights of the sensory information were not different between the groups; however, in the group with FM, higher pain intensity was associated with smaller tactile weight. This study shows no alterations of the visuotactile integration in individuals with FM, though pain may influence tactile weight in these participants.

Linear Mixed-Effects Models for Dependent Data: Power and Accuracy in Parameter Estimation

Linear mixed-effects models have been increasingly used to analyze dependent data in psychological research. Despite their many advantages over ANOVA, critical issues in their analyses remain. Due to increasing random effects and model complexity, estimation computation is demanding, and convergence becomes challenging. Applied users need help choosing appropriate methods to estimate random effects. The present Monte Carlo simulation study investigated the impacts when the restricted maximum likelihood (REML) and Bayesian estimation models were misspecified in the estimation. We also compared the performance of Akaike information criterion (AIC) and deviance information criterion (DIC) in model selection. Results showed that models neglecting the existing random effects had inflated Type I errors, unacceptable coverage, and inaccurate R-squared measures of fixed and random effects variation. Furthermore, models with redundant random effects had convergence problems, lower statistical power, and inaccurate R-squared measures for Bayesian estimation. The convergence problem is more severe for REML, while reduced power and inaccurate R-squared measures were more severe for Bayesian estimation. Notably, DIC was better than AIC in identifying the true models (especially for models including person random intercept only), improving convergence rates, and providing more accurate effect size estimates, despite AIC having higher power than DIC with 10 items and the most complicated true model.

Effects of older age on visual and self-motion sensory cue integration in navigation.

Older adults demonstrate impairments in navigation that cannot be explained by general cognitive and motor declines. Previous work has shown that older adults may combine sensory cues during navigation differently than younger adults, though this work has largely been done in dark environments where sensory integration may differ from full-cue environments. Here, we test whether aging adults optimally combine cues from two sensory systems critical for navigation: vision (landmarks) and body-based self-motion cues. Participants completed a homing (triangle completion) task using immersive virtual reality to offer the ability to navigate in a well-lit environment including visibility of the ground plane. An optimal model, based on principles of maximum-likelihood estimation, predicts that precision in homing should increase with multisensory information in a manner consistent with each individual sensory cue's perceived reliability (measured by variability). We found that well-aging adults (with normal or corrected-to-normal sensory acuity and active lifestyles) were more variable and less accurate than younger adults during navigation. Both older and younger adults relied more on their visual systems than a maximum likelihood estimation model would suggest. Overall, younger adults' visual weighting matched the model's predictions whereas older adults showed sub-optimal sensory weighting. In addition, high inter-individual differences were seen in both younger and older adults. These results suggest that older adults do not optimally weight each sensory system when combined during navigation, and that older adults may benefit from interventions that help them recalibrate the combination of visual and self-motion cues for navigation.

A novel predictive model of lymphovascular space invasion in early-stage endometrial cancer.

To predict lymphovascular space invasion (LVSI) positivity in early-stage (stage 1-2) endometrial cancer (EC) using a predictive model with prognostic factors of EC. We included 461 patients who underwent total hysterectomy and bilateral salpingo-oophorectomy with pelvic-paraaortic lymphadenectomy as the primary treatment for presumed early-stage EC at our clinic between 2010 and 2020. Moreover, all surgical specimens were examined histopathologically for the positivity or negativity of LVSI, and the patients were divided into two groups based on these pathologic outcomes. Age, menopausal status, histological type (type 1-2), histological grade (grades 1-2-3), depth of myometrial invasion, and peritoneal cytology results were recorded and analyzed as clinicopathological and demographic characteristics of the patients. The Loess algorithm determined the relationship between the observed and predicted outcomes. The distinction between the algorithms was evaluated by calculating the C-index. LVSI positivity was significantly associated with advanced age, menopause, type 2 EC, advanced histological grade, malignant peritoneal cytology, cervical involvement, and a tumor exceeding 50% of the myometrial depth (p<0.001, respectively). Remarkably, LVSI was most strongly associated with three explanatory variables: 1- More than 50% myometrial invasion [odds ratio (OR): 3.78; 95% confidence interval (CI): 1.80-7.60], 2- Advanced histological grade [OR=1.98 (1.20-3.20) 95% CI], 3- Malignant peritoneal cytology [OR= 3.06 (1.40-6.30) 95% CI]. The penalized maximum likelihood estimation model correctly classified 87% of the included patients (C-index: 0.876). Our predictive model may help predict LVSI based on different prognostic factors. The prognostic factors included in the nomogram were significantly associated with LVSI, particularly myometrial invasion depth of more than 50%, advanced histological grade, and malignant peritoneal cytology.

Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors, site quality, and aridity index

The diameter distribution function (DDF) is a crucial tool for accurately predicting stand carbon storage (CS). The current key issue, however, is how to construct a high-precision DDF based on stand factors, site quality, and aridity index to predict stand CS in multi-species mixed forests with complex structures. This study used data from 70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest, Hebei Province, China, to construct the DDF based on maximum likelihood estimation and finite mixture model (FMM). Ordinary least squares (OLS), linear seemingly unrelated regression (LSUR), and back propagation neural network (BPNN) were used to investigate the influences of stand factors, site quality, and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests. The results showed that FMM accurately described the stand-level diameter distribution of the mixed P. davidiana and B. platyphylla forests; whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution. The combined variable of quadratic mean diameter (Dq), stand basal area (BA), and site quality improved the accuracy of the shape parameter models of FMM; the combined variable of Dq, BA, and De Martonne aridity index improved the accuracy of the scale parameter models. Compared to OLS and LSUR, the BPNN had higher accuracy in the re-parameterization process of FMM. OLS, LSUR, and BPNN overestimated the CS of P. davidiana but underestimated the CS of B. platyphylla in the large diameter classes (DBH ≥18 ​cm). BPNN accurately estimated stand- and species-level CS, but it was more suitable for estimating stand-level CS compared to species-level CS, thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.

Model Regresi Cox Untuk Data Masa Studi (Studi Kasus: Data Masa Studi Mahasiswa Fakultas Teknik Universitas Bangka Belitung)

Student study time is the time needed by students to complete their education, which starts from the time they enter college until they are declared graduated or have completed their study period. In the study period data, survival time observations were only carried out partially or not until the failure event. In other words, termination occurs until the observation deadline. This termination occurred due to several factors that allegedly influenced the student's study period. Using study period data for students of the Faculty of Engineering, University of Bangka Belitung, class of 2015/2016, this study used the Kaplan Meier Estimation to see the survival function of each factor causing the length of study period graphically and the Log Rank Test statistically. Meanwhile, to look at the factors that determine the length of a student's study period, researchers used the Cox Regression and Maximum Likelihood Estimation (MLE) models to find the best model. The results of the data analysis show that there are differences in the survival function in each category for all variables graphically, while the statistical comparison of the results of the estimation of the survival function curve based on gender and organizational status is not significantly different. The results of the analysis also show that the proportional hazard assumption is fulfilled through the cumulative hazard log so that categorical variables can be used in the Cox Regression model. Based on the results of the likelihood estimation, the variables that have a significant effect on the study period of Engineering Faculty students are majors and GPA variables. Furthermore, from the interpretation of the model parameters, it is obtained that the Hazard Ratio (HR) value for the study period of Mechanical, Mining and Electrical Engineering students is faster than that of Civil Engineering students, while students with GPA ≥ 3.00 have a shorter study period than students with GPA &lt; 3.00.

Associations between blood pressure control and clinical events suggestive of nutrition care documented in electronic health records of patients with hypertension

BackgroundClinical events suggestive of nutrition care found in electronic health records (EHRs) are rarely explored for their associations with hypertension outcomes.MethodsLongitudinal analysis using structured EHR data from primary care visits at a health system in the US from December 2017—December 2020 of adult patients with hypertension (n = 4,237) tested for associations between last visit blood pressure (BP) control (≤ 140 Systolic BP and ≤ 90 Diastolic BP) and ≥ 1 nutrition care clinical event operationalized as (overweight or obesity (BMI > 25 or 30, respectively) diagnoses, preventive care visits, or provision of patient education materials (PEM)). Descriptive statistics and longitudinal targeted maximum likelihood estimation (LTMLE) models were conducted to explore average treatment effects (ATE) of timing and dose response from these clinical events on blood pressure control overall and by race.ResultsThe median age was 62 years, 29% were male, 52% were Black, 25% were from rural areas and 50% had controlled BP at baseline. Annual documentation of overweight/obesity diagnoses ranged 3.0–7.8%, preventive care visits ranged 6.2–15.7%, and PEM with dietary and hypertension content were distributed to 8.5–28.8% patients. LTMLE models stratified by race showed differences in timing, dose, and type of nutrition care. Black patients who had nutrition care in Year 3 only compared to none had lower odds for BP control (ATE -0.23, 95% CI: -0.38,-0.08, p = 0.003), preventive visits in the last 2 years high higher odds for BP control (ATE 0.31, 95% CI: 0.07,0.54, p = 0.01), and early or late PEMs had lower odds for BP control (ATE -0.08, 95% CI: -0.15,-0.01, p = 0.03 and ATE -0.23, 95% CI: -0.41,-0.05, p = 0.01, respectively).ConclusionsIn this study, clinical events suggestive of nutrition care are significantly associated with BP control, but are infrequent and effects differ by type, timing, and patient race. Preventive visits appear to have the most effect; additional research should include examining clinical notes for evidence of nutrition care among different populations, which may uncover areas for improving nutrition care for patients with chronic disease.

Near-optimal integration of the magnitude information of time and numerosity.

Magnitude information is often correlated in the external world, providing complementary information about the environment. As if to reflect this relationship, the perceptions of different magnitudes (e.g. time and numerosity) are known to influence one another. Recent studies suggest that such magnitude interaction is similar to cue integration, such as multisensory integration. Here, we tested whether human observers could integrate the magnitudes of two quantities with distinct physical units (i.e. time and numerosity) as abstract magnitude information. The participants compared the magnitudes of two visual stimuli based on time, numerosity, or both. Consistent with the predictions of the maximum-likelihood estimation model, the participants integrated time and numerosity in a near-optimal manner; the weight of each dimension was proportional to their relative reliability, and the integrated estimate was more reliable than either the time or numerosity estimate. Furthermore, the integration approached a statistical optimum as the temporal discrepancy of the acquisition of each piece of information became smaller. These results suggest that magnitude interaction arises through a similar computational mechanism to cue integration. They are also consistent with the idea that different magnitudes are processed by a generalized magnitude system.

Perceiving depth from texture and disparity cues: Evidence for a non-probabilistic account of cue integration.

Bayesian inference theories have been extensively used to model how the brain derives three-dimensional (3D) information from ambiguous visual input. In particular, the maximum likelihood estimation (MLE) model combines estimates from multiple depth cues according to their relative reliability to produce the most probable 3D interpretation. Here, we tested an alternative theory of cue integration, termed the intrinsic constraint (IC) theory, which postulates that the visual system derives the most stable, not most probable, interpretation of the visual input amid variations in viewing conditions. The vector sum model provides a normative approach for achieving this goal where individual cue estimates are components of a multidimensional vector whose norm determines the combined estimate. Individual cue estimates are not accurate but related to distal 3D properties through a deterministic mapping. In three experiments, we show that the IC theory can more adeptly account for 3D cue integration than MLE models. In Experiment 1, we show systematic biases in the perception of depth from texture and depth from binocular disparity. Critically, we demonstrate that the vector sum model predicts an increase in perceived depth when these cues are combined. In Experiment 2, we illustrate the IC theory radical reinterpretation of the just noticeable difference (JND) and test the related vector sum model prediction of the classic finding of smaller JNDs for combined-cue versus single-cue stimuli. In Experiment 3, we confirm the vector sum prediction that biases found in cue integration experiments cannot be attributed to flatness cues, as the MLE model predicts.

Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants

BackgroundThe serial interval is the period of time between symptom onset in the primary case and symptom onset in the secondary case. Understanding the serial interval is important for determining transmission dynamics of infectious diseases like COVID-19, including the reproduction number and secondary attack rates, which could influence control measures. Early meta-analyses of COVID-19 reported serial intervals of 5.2 days (95% CI: 4.9–5.5) for the original wild-type variant and 5.2 days (95% CI: 4.87–5.47) for Alpha variant. The serial interval has been shown to decrease over the course of an epidemic for other respiratory diseases, which may be due to accumulating viral mutations and implementation of more effective nonpharmaceutical interventions. We therefore aggregated the literature to estimate serial intervals for Delta and Omicron variants.MethodsThis study followed Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A systematic literature search was conducted of PubMed, Scopus, Cochrane Library, ScienceDirect, and preprint server medRxiv for articles published from April 4, 2021, through May 23, 2023. Search terms were: (“serial interval” or “generation time”), (“Omicron” or “Delta”), and (“SARS-CoV-2” or “COVID-19”). Meta-analyses were done for Delta and Omicron variants using a restricted maximum-likelihood estimator model with a random effect for each study. Pooled average estimates and 95% confidence intervals (95% CI) are reported.ResultsThere were 46,648 primary/secondary case pairs included for the meta-analysis of Delta and 18,324 for Omicron. Mean serial interval for included studies ranged from 2.3–5.8 days for Delta and 2.1–4.8 days for Omicron. The pooled mean serial interval for Delta was 3.9 days (95% CI: 3.4–4.3) (20 studies) and Omicron was 3.2 days (95% CI: 2.9–3.5) (20 studies). Mean estimated serial interval for BA.1 was 3.3 days (95% CI: 2.8–3.7) (11 studies), BA.2 was 2.9 days (95% CI: 2.7–3.1) (six studies), and BA.5 was 2.3 days (95% CI: 1.6–3.1) (three studies).ConclusionsSerial interval estimates for Delta and Omicron were shorter than ancestral SARS-CoV-2 variants. More recent Omicron subvariants had even shorter serial intervals suggesting serial intervals may be shortening over time. This suggests more rapid transmission from one generation of cases to the next, consistent with the observed faster growth dynamic of these variants compared to their ancestors. Additional changes to the serial interval may occur as SARS-CoV-2 continues to circulate and evolve. Changes to population immunity (due to infection and/or vaccination) may further modify it.

Impact of life-sustaining therapies on critically ill patients with cancer.

e13590 Background: Advances in diagnosis and treatment have led to large reductions in mortality rates for patients with cancer, resulting in a steady increase in patients admitted to intensive care units (ICUs). However, there is conflicting evidence supporting the benefit of common life-sustaining therapies for critically ill patients, within this cohort. We examined differences in treatment effects of life-sustaining therapies between critically ill septic patients with and without cancer. Methods: Adults aged 18+ with a principal cancer diagnosis from 2008 - 2019, admitted to the ICU for sepsis, were extracted from publicly accessible ICU databases: Medical Information Mart for Intensive Care IV (MIMIC-IV) and the eICU Collaborative Research Database (eICU-CRD). Logistic regression estimated the likelihood of receiving mechanical ventilation (MV), renal replacement therapy (RRT) or vasopressors (VP) during ICU admission. Additionally targeted maximum likelihood estimation (TMLE) models estimated average treatment effects of MV, VP, and RRT on mortality. As TMLE uses machine learning models, it accommodates large numbers of covariates with complex, non-linear relationships. The method outputs Average treatment effect (ATE), an odds ratio representing the mean difference in outcomes in a hypothetical world in which all patients received treatment, compared to a hypothetical world in which no patients received treatment. Models were adjusted for age, sex, ethnicity, Charlson Comorbidity Index, SOFA score, hypertension, heart failure, asthma, COPD, CKD, and code status at admission. Results: A total of 7,120 adults met inclusion criteria. Septic patients with cancer did not have a significantly different likelihood of receiving MV (aOR [95%CI], 0.94 [0.6 - 1.46]), RRT (0.79, [0.33 - 1.93]), or VP (1.09, [0.74, 1.9]) than septic patients without cancer. Among patients with low SOFA scores, those with cancer were more likely than those without cancer to benefit from RRT. Otherwise, there was no statistically significant difference in mortality with the use of MV, RRT, and VP between patients with and without cancer (p&gt;0.05). Conclusions: To our knowledge this is the first study to utilize contemporary, nationwide critical care data to establish a causal relationship between mortality and the use of common life-sustaining therapies for patients with cancer. Our study highlights the tremendous advances in cancer treatment over the last decade, leading to similar effects of common critical care interventions on mortality, regardless of cancer diagnosis. [Table: see text]

A target parameter estimation algorithm for integration of radar and communication based on orthogonal time frequency space

AbstractOrthogonal time frequency space (OTFS) is a new method technique that supports reliable information transmission in a strong Doppler environment. Aiming at the radar target parameter estimation problem of integration of radar and communication (IRC) based on OTFS, this paper proposes a two‐step radar target parameter estimation algorithm combining maximum likelihood (ML) estimation and gradient descent (GD) principle. Firstly, the echo signal of the OTFS integrated signal is constructed and based on this, the ML estimation model is constructed, and the alternating projection (AP) algorithm is employed to make a rough estimation of the target parameters. Then the rough estimation results of the AP‐ML algorithm are employed as the initial value of the GD algorithm for the fine parameter estimation. Simulation results show that the proposed algorithm has better target parameter estimation performance and resolution while reducing the computational complexity.

A general algorithm for error-in-variables regression modelling using Monte Carlo expectation maximization.

In regression modelling, measurement error models are often needed to correct for uncertainty arising from measurements of covariates/predictor variables. The literature on measurement error (or errors-in-variables) modelling is plentiful, however, general algorithms and software for maximum likelihood estimation of models with measurement error are not as readily available, in a form that they can be used by applied researchers without relatively advanced statistical expertise. In this study, we develop a novel algorithm for measurement error modelling, which could in principle take any regression model fitted by maximum likelihood, or penalised likelihood, and extend it to account for uncertainty in covariates. This is achieved by exploiting an interesting property of the Monte Carlo Expectation-Maximization (MCEM) algorithm, namely that it can be expressed as an iteratively reweighted maximisation of complete data likelihoods (formed by imputing the missing values). Thus we can take any regression model for which we have an algorithm for (penalised) likelihood estimation when covariates are error-free, nest it within our proposed iteratively reweighted MCEM algorithm, and thus account for uncertainty in covariates. The approach is demonstrated on examples involving generalized linear models, point process models, generalized additive models and capture-recapture models. Because the proposed method uses maximum (penalised) likelihood, it inherits advantageous optimality and inferential properties, as illustrated by simulation. We also study the model robustness of some violations in predictor distributional assumptions. Software is provided as the refitME package on R, whose key function behaves like a refit() function, taking a fitted regression model object and re-fitting with a pre-specified amount of measurement error.

CSCARY: A New Algorithm Combining Observables of ASCAT and CyGNSS for More Accurate Ocean Surface Wind Speed Retrieval

This letter presents a new ocean surface wind speed (OSWS) retrieval algorithm called Combined Scatterometry Reflectometry (CSCARY). The algorithm uses the maximum-likelihood estimator and geophysical model functions to combine backward and forward observables from the Advanced Scatterometer (ASCAT) and the Cyclone Global Navigation Satellite System (CyGNSS) to achieve more accurate wind speed retrieval. To verify the efficacy of the CSCARY algorithm, a case study is conducted on part of the South China Sea. Results showed that the algorithm improved wind speed accuracy by 28.22% for low-medium wind speeds (2-10 m/s) and 25.33% for medium-high wind speeds (6-16 m/s) compared to the operational ASCAT algorithm. Additionally, the most significant improvement is observed at medium wind speeds, with an increase of 23.6%. These findings suggest that combining scatterometer and GNSS-R could lead to better OSWS retrieval.

1258: USE OF CAUSAL INFERENCE TO COMPARE NOREPINEPHRINE- AND VASOPRESSIN-RELATED ICU MORTALITY

Introduction: The choice of vasopressor has been studied extensively in septic shock, however limited data exists in other shock types. Norepinephrine and vasopressin were compared in a retrospective study of the cardiac surgery population. Vasopressin as first-line treatment of post-cardiac surgery vasoplegic shock was found to improve clinical outcomes. However, the study was associated with biases which limits its validity. As there are no prospective studies, we use causal inference to analyze retrospective data. An observational study allows individual observation under one treatment scenario, while a counterfactual outcome is unknown. Targeted Maximum Likelihood Estimation (TMLE) models counterfactual outcomes with limited confounding by generating a pseudo-population. By adjusting for common causes of death, we were able to compare mortality outcomes between norepinephrine and vasopressin. Methods: An anonymous, publicly available database was analyzed using pre-existing IRB approval. The eICU Collaborative Research Database contains data on over 200,000 ICU admissions. The analysis used 27,388 patients with 25 initial features. Missing values greater than 50% were dropped. Machine Learning Extensions Library (MLxtend) was used to select features sequentially. The primary outcome was ICU mortality. Imputation was performed using Sklearn Iterative Imputuer, data scaled using Sklearn Standard Scaler. Our primary model was TMLE with Logistic Regression. We analyzed vasopressin and norepinephrine administration, accounting for their co-administration by using them as covariates. Net fluid balance, Apache IV, albumin, admission weight, age, diabetes, and metastatic cancer were confounder covariates for both groups. Before analysis, a causal diagram was constructed based on these covariates. Our primary metric was Average Treatment Effect (ATE). Results: Mortality analysis on the norepinephrine group showed an ATE of -0.179, 95% CI [-0.188, -0.167]. Vasopressin showed an ATE of 0.03 (95% CI [0.01,0.051]). Compared to vasopressin, norepinephrine showed a reduction in mortality. Conclusions: We found that norepinephrine was associated with decreased mortality compared to vasopressin in this setting. We hope this study’s results will help guide prospective studies moving forward.

The case against probabilistic inference: a new deterministic theory of 3D visual processing.

How the brain derives 3D information from inherently ambiguous visual input remains the fundamental question of human vision. The past two decades of research have addressed this question as a problem of probabilistic inference, the dominant model being maximum-likelihood estimation (MLE). This model assumes that independent depth-cue modules derive noisy but statistically accurate estimates of 3D scene parameters that are combined through a weighted average. Cue weights are adjusted based on the system representation of each module's output variability. Here I demonstrate that the MLE model fails to account for important psychophysical findings and, importantly, misinterprets the just noticeable difference, a hallmark measure of stimulus discriminability, to be an estimate of perceptual uncertainty. I propose a new theory, termed Intrinsic Constraint, which postulates that the visual system does not derive the most probable interpretation of the visual input, but rather, the most stable interpretation amid variations in viewing conditions. This goal is achieved with the Vector Sum model, which represents individual cue estimates as components of a multi-dimensional vector whose norm determines the combined output. This model accounts for the psychophysical findings cited in support of MLE, while predicting existing and new findings that contradict the MLE model. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

Learning multisensory cue integration: A computational model of crossmodal synaptic plasticity enables reliability-based cue weighting by capturing stimulus statistics.

The brain forms unified, coherent, and accurate percepts of events occurring in the environment by integrating information from multiple senses through the process of multisensory integration. The neural mechanisms underlying this process, its development and its maturation in a multisensory environment are yet to be properly understood. Numerous psychophysical studies suggest that the multisensory cue integration process follows the principle of Bayesian estimation, where the contributions of individual sensory modalities are proportional to the relative reliabilities of the different sensory stimuli. In this article I hypothesize that experience dependent crossmodal synaptic plasticity may be a plausible mechanism underlying development of multisensory cue integration. I test this hypothesis via a computational model that implements Bayesian multisensory cue integration using reliability-based cue weighting. The model uses crossmodal synaptic plasticity to capture stimulus statistics within synaptic weights that are adapted to reflect the relative reliabilities of the participating stimuli. The model is embodied in a simulated robotic agent that learns to localize an audio-visual target by integrating spatial location cues extracted from of auditory and visual sensory modalities. Results of multiple randomized target localization trials in simulation indicate that the model is able to learn modality-specific synaptic weights proportional to the relative reliabilities of the auditory and visual stimuli. The proposed model with learned synaptic weights is also compared with a maximum-likelihood estimation model for cue integration via regression analysis. Results indicate that the proposed model reflects maximum-likelihood estimation.

Some Consequences of Including Impulse-Indicator Dummy Variables in Econometric Models

Suppose that a regression model includes a regressor that is a dummy variable that takes a non-zero value for only one observation. Then the least squares estimates of the coefficients of the other regressors are the same as would be obtained by dropping that observation from the sample and omitting the dummy variable. This is well-known, but is frequently overlooked by practitioners. In this note we extend this result to the case of instrumental variables estimation, and to maximum likelihood estimation of models for count data, binary dependent variables, and duration data. These extensions also allow for the inclusion of many such “impulse-indicator” variables in the model, not just one.

An Occupancy-Based Adaptive Signal Control for a Congested Signalized Intersection in the Low CV Penetration Environment

Adaptive signal control (ASC) is a well-researched topic that offers an efficient way for traffic management. It possesses a powerful ability to accommodate complex and constantly changing urban transportation networks. With the development of vehicular communication, CV-based ASC shows remarkable advantages compared with the traditional ASC system. Though the existing CV-based ASC strategies were proposed in the past few years, however, there are still issues to overcome. Most of the studies on CV-based ASC are based on the assumption of high CV penetration rate, which often result in poor performance when applied to low CV penetration environments. Besides, the lack of consideration for mixed traffic flow, which is in terms of both the vehicle types and CV penetration of different types of vehicles. To solve these issues, this paper developed an Occupancy-Based ASC strategy for a congested signalized intersection to optimize signal timing and reduce total passenger delay in the low CV penetration environment. Focused on the issues existing in the low CV penetration environment, a Maximum Likelihood Estimation (MLE) model was proposed to estimate vehicle arrivals, and two traffic models, MicroDM and MacroDM, were developed to model the mixed traffic flow and estimate passenger delay. With the purpose of offering fair treatment to passengers approaching the intersection, we proposed an Occupancy-Based Adaptive Signal Control strategy. By transforming the complex signal control problem into a mixed-integer linear programming problem, we found the optimal solution for minimizing total passenger delay. We then evaluated the proposed Occupancy-Based ASC strategy using simulation case studies. The results show that changing traffic status could be captured and estimated with the real-time CV trajectory data as input. Applying the Occupancy-Based ASC control strategy, phases with HOVs or more vehicles will be allocated more travel time. In particular, optimization results show that the proposed Occupancy-Based ASC strategy effectively balances passenger travel demands during peak volume periods.