scholarly journals Dissimilarity for functional data clustering based on smoothing parameter commutation

2017 ◽  
Vol 27 (11) ◽  
pp. 3492-3504 ◽  
Author(s):  
ShengLi Tzeng ◽  
Christian Hennig ◽  
Yu-Fen Li ◽  
Chien-Ju Lin
Keyword(s):  
Functional Data ◽  
Data Clustering ◽  
Smoothing Splines ◽  
Smoothing Parameter ◽  
Multiple Time ◽  
Easy Method ◽  
Time Points ◽  
Methadone Dosage ◽  
Estimation Uncertainty ◽  
Smoothing Parameters

Many studies measure the same type of information longitudinally on the same subject at multiple time points, and clustering of such functional data has many important applications. We propose a novel and easy method to implement dissimilarity measure for functional data clustering based on smoothing splines and smoothing parameter commutation. This method handles data observed at regular or irregular time points in the same way. We measure the dissimilarity between subjects based on varying curve estimates with pairwise commutation of smoothing parameters. The intuition is that smoothing parameters of smoothing splines reflect the inverse of the signal-to-noise ratios and that when applying an identical smoothing parameter the smoothed curves for two similar subjects are expected to be close. Our method takes into account the estimation uncertainty using smoothing parameter commutation and is not strongly affected by outliers. It can also be used for outlier detection. The effectiveness of our proposal is shown by simulations comparing it to other dissimilarity measures and by a real application to methadone dosage maintenance levels.

scholarly journals Robust fitting for generalized additive models for location, scale and shape

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
William H. Aeberhard ◽  
Eva Cantoni ◽  
Giampiero Marra ◽  
Rosalba Radice
Keyword(s):  
Generalized Additive Models ◽  
Nonlinear Effects ◽  
Smoothing Splines ◽  
Parameter Selection ◽  
Information Criteria ◽  
Smoothing Parameter ◽  
Additive Models ◽  
Robust Estimators ◽  
Smoothing Parameter Selection ◽  
Smoothing Parameters

AbstractThe validity of estimation and smoothing parameter selection for the wide class of generalized additive models for location, scale and shape (GAMLSS) relies on the correct specification of a likelihood function. Deviations from such assumption are known to mislead any likelihood-based inference and can hinder penalization schemes meant to ensure some degree of smoothness for nonlinear effects. We propose a general approach to achieve robustness in fitting GAMLSSs by limiting the contribution of observations with low log-likelihood values. Robust selection of the smoothing parameters can be carried out either by minimizing information criteria that naturally arise from the robustified likelihood or via an extended Fellner–Schall method. The latter allows for automatic smoothing parameter selection and is particularly advantageous in applications with multiple smoothing parameters. We also address the challenge of tuning robust estimators for models with nonlinear effects by proposing a novel median downweighting proportion criterion. This enables a fair comparison with existing robust estimators for the special case of generalized additive models, where our estimator competes favorably. The overall good performance of our proposal is illustrated by further simulations in the GAMLSS setting and by an application to functional magnetic resonance brain imaging using bivariate smoothing splines.

scholarly journals Quantitative Checklist for Autism in Toddlers (Q-CHAT). A population screening study with follow-up: the case for multiple time-point screening for autism

2021 ◽  
Vol 5 (1) ◽  
pp. e000700
Author(s):  
Carrie Allison ◽  
Fiona E Matthews ◽  
Liliana Ruta ◽  
Greg Pasco ◽  
Renee Soufer ◽  
...  
Keyword(s):  
Phase 1 ◽  
Autism Spectrum ◽  
Population Screening ◽  
Diagnostic Assessment ◽  
Test Accuracy ◽  
Multiple Time ◽  
Phase 2 ◽  
Time Points ◽  
Screening Study

ObjectiveThis is a prospective population screening study for autism in toddlers aged 18–30 months old using the Quantitative Checklist for Autism in Toddlers (Q-CHAT), with follow-up at age 4.DesignObservational study.SettingLuton, Bedfordshire and Cambridgeshire in the UK.Participants13 070 toddlers registered on the Child Health Surveillance Database between March 2008 and April 2009, with follow-up at age 4; 3770 (29%) were screened for autism at 18–30 months using the Q-CHAT and the Childhood Autism Spectrum Test (CAST) at follow-up at age 4.InterventionsA stratified sample across the Q-CHAT score distribution was invited for diagnostic assessment (phase 1). The 4-year follow-up included the CAST and the Checklist for Referral (CFR). All with CAST ≥15, phase 1 diagnostic assessment or with developmental concerns on the CFR were invited for diagnostic assessment (phase 2). Standardised diagnostic assessment at both time-points was conducted to establish the test accuracy of the Q-CHAT.Main outcome measuresConsensus diagnostic outcome at phase 1 and phase 2.ResultsAt phase 1, 3770 Q-CHATs were returned (29% response) and 121 undertook diagnostic assessment, of whom 11 met the criteria for autism. All 11 screened positive on the Q-CHAT. The positive predictive value (PPV) at a cut-point of 39 was 17% (95% CI 8% to 31%). At phase 2, 2005 of 3472 CASTs and CFRs were returned (58% response). 159 underwent diagnostic assessment, including 82 assessed in phase 1. All children meeting the criteria for autism identified via the Q-CHAT at phase 1 also met the criteria at phase 2. The PPV was 28% (95% CI 15% to 46%) after phase 1 and phase 2.ConclusionsThe Q-CHAT can be used at 18–30 months to identify autism and enable accelerated referral for diagnostic assessment. The low PPV suggests that for every true positive there would, however, be ~4–5 false positives. At follow-up, new cases were identified, illustrating the need for continued surveillance and rescreening at multiple time-points using developmentally sensitive instruments. Not all children who later receive a diagnosis of autism are detectable during the toddler period.

scholarly journals Use of TanDEM-X and SRTM-C Data for Detection of Deforestation Caused by Bark Beetle in Central European Mountains

2021 ◽  
Vol 13 (15) ◽  
pp. 3042
Author(s):  
Kateřina Gdulová ◽  
Jana Marešová ◽  
Vojtěch Barták ◽  
Marta Szostak ◽  
Jaroslav Červenka ◽  
...  
Keyword(s):  
Bark Beetle ◽  
Synergistic Effects ◽  
Canopy Height ◽  
Surface Model ◽  
Multiple Time ◽  
Central European ◽  
Time Points ◽  
Bohemian Forest ◽  
Multiple Time Points ◽  
Surrounding Areas

The availability of global digital elevation models (DEMs) from multiple time points allows their combination for analysing vegetation changes. The combination of models (e.g., SRTM and TanDEM-X) can contain errors, which can, due to their synergistic effects, yield incorrect results. We used a high-resolution LiDAR-derived digital surface model (DSM) to evaluate the accuracy of canopy height estimates of the aforementioned global DEMs. In addition, we subtracted SRTM and TanDEM-X data at 90 and 30 m resolutions, respectively, to detect deforestation caused by bark beetle disturbance and evaluated the associations of their difference with terrain characteristics. The study areas covered three Central European mountain ranges and their surrounding areas: Bohemian Forest, Erzgebirge, and Giant Mountains. We found that vertical bias of SRTM and TanDEM-X, relative to the canopy height, is similar with negative values of up to −2.5 m and LE90s below 7.8 m in non-forest areas. In forests, the vertical bias of SRTM and TanDEM-X ranged from −0.5 to 4.1 m and LE90s from 7.2 to 11.0 m, respectively. The height differences between SRTM and TanDEM-X show moderate dependence on the slope and its orientation. LE90s for TDX-SRTM differences tended to be smaller for east-facing than for west-facing slopes, and varied, with aspect, by up to 1.5 m in non-forest areas and 3 m in forests, respectively. Finally, subtracting SRTM and NASA DEMs from TanDEM-X and Copernicus DEMs, respectively, successfully identified large areas of deforestation caused by hurricane Kyril in 2007 and a subsequent bark beetle disturbance in the Bohemian Forest. However, local errors in TanDEM-X, associated mainly with forest-covered west-facing slopes, resulted in erroneous identification of deforestation. Therefore, caution is needed when combining SRTM and TanDEM-X data in multitemporal studies in a mountain environment. Still, we can conclude that SRTM and TanDEM-X data represent suitable near global sources for the identification of deforestation in the period between the time points of their acquisition.

Meta-analysis of effect sizes reported at multiple time points: A multivariate approach

2012 ◽  
Vol 9 (5) ◽  
pp. 610-620 ◽  
Author(s):  
Thomas A Trikalinos ◽  
Ingram Olkin
Keyword(s):  
Random Effects ◽  
Treatment Effects ◽  
Multivariate Analyses ◽  
Meta Analysis ◽  
Effect Sizes ◽  
Multiple Time ◽  
Time Points ◽  
Fixed And Random Effects ◽  
Multiple Time Points ◽  
Meta Analyses

Background Many comparative studies report results at multiple time points. Such data are correlated because they pertain to the same patients, but are typically meta-analyzed as separate quantitative syntheses at each time point, ignoring the correlations between time points. Purpose To develop a meta-analytic approach that estimates treatment effects at successive time points and takes account of the stochastic dependencies of those effects. Methods We present both fixed and random effects methods for multivariate meta-analysis of effect sizes reported at multiple time points. We provide formulas for calculating the covariance (and correlations) of the effect sizes at successive time points for four common metrics (log odds ratio, log risk ratio, risk difference, and arcsine difference) based on data reported in the primary studies. We work through an example of a meta-analysis of 17 randomized trials of radiotherapy and chemotherapy versus radiotherapy alone for the postoperative treatment of patients with malignant gliomas, where in each trial survival is assessed at 6, 12, 18, and 24 months post randomization. We also provide software code for the main analyses described in the article. Results We discuss the estimation of fixed and random effects models and explore five options for the structure of the covariance matrix of the random effects. In the example, we compare separate (univariate) meta-analyses at each of the four time points with joint analyses across all four time points using the proposed methods. Although results of univariate and multivariate analyses are generally similar in the example, there are small differences in the magnitude of the effect sizes and the corresponding standard errors. We also discuss conditional multivariate analyses where one compares treatment effects at later time points given observed data at earlier time points. Limitations Simulation and empirical studies are needed to clarify the gains of multivariate analyses compared with separate meta-analyses under a variety of conditions. Conclusions Data reported at multiple time points are multivariate in nature and are efficiently analyzed using multivariate methods. The latter are an attractive alternative or complement to performing separate meta-analyses.

THE ROLE OF MEMORY PERSPECTIVE IN SOCIAL PHOBIA: IMMEDIATE AND DELAYED MEMORIES FOR ROLE-PLAYED SITUATIONS

2002 ◽  
Vol 30 (4) ◽  
pp. 415-425 ◽  
Author(s):  
Meredith E. Coles ◽  
Cynthia L. Turk ◽  
Richard G. Heimberg
Keyword(s):  
Social Phobia ◽  
Multiple Time ◽  
Behavioral Models ◽  
Social Situations ◽  
Time Points ◽  
Cognitive Behavioral Models ◽  
Multiple Time Points ◽  
Memory Perspective ◽  
Field Perspective

Cognitive-behavioral models (Clark & Wells, 1995; Rapee & Heimberg, 1997) and recent research suggest that individuals with social phobia (SP) experience both images (Hackmann, Surawy, & Clark, 1998) and memories (Coles, Turk, Heimberg, & Fresco, 2001; Wells, Clark, & Ahmad, 1998) of anxiety-producing social situations from an observer perspective. The current study examines memory perspective for two role-played situations (speech and social interaction) at multiple time points (immediate and 3 weeks post) in 22 individuals with generalized SP and 30 non-anxious controls (NACs). At both time points, SPs recalled the role-plays from a more observer/less field perspective than did NACs. Further, over time, the memory perspective of SPs became even more observer/less field while the memory perspective of NAC remained relatively stable.

“The Effect of Obstructive Sleep Apnea on Peripheral Blood Amino Acid and Biogenic Amine Metabolome at Multiple Time Points Overnight“

2020 ◽  
Author(s):  
Ott Kiens ◽  
Egon Taalberg ◽  
Viktoria Ivanova ◽  
Ketlin Veeväli ◽  
Triin Laurits ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Amino Acid ◽  
Sleep Apnea ◽  
Biogenic Amines ◽  
Peripheral Blood ◽  
Apnea Hypopnea Index ◽  
Multiple Time ◽  
Blood Concentrations ◽  
Time Points ◽  
Obstructive Sleep

Abstract There are no clinical studies that have investigated the differences in blood serum metabolome between obstructive sleep apnea (OSA) patients and controls. In a single-center prospective observational study, we compared metabolomic profiles in the peripheral blood of OSA patients with apnea-hypopnea index (AHI) > 15/h and control individuals. Blood was obtained at 3 different time points overnight: 21:00 p.m.; 5:00 a.m. and 7:00 a.m. We used a targeted approach for detecting amino acids and biogenic amines and analyzed the data with ranked general linear model for repeated measures. We recruited 31 patients with moderate-to-severe OSA and 32 controls. Significant elevations in median concentrations of alanine, proline and kynurenine in OSA patients compared to controls were detected. Significant changes in the overnight dynamics of peripheral blood concentrations occurred in OSA: glutamine, serine, threonine, tryptophan, kynurenine and glycine levels increased, whereas a fall occurred in the same biomarker levels in controls. Phenylalanine and proline levels decreased slightly, compared to a steeper fall in controls. The study indicates that serum profiles of amino acid and biogenic amines are significantly altered in patients with OSA referring to vast pathophysiologic shifts reflected in the systemic metabolism.

The Silicon Peaches

2021 ◽  
pp. 31-42
Author(s):  
Dan Breznitz
Keyword(s):  
Information And Communication Technologies ◽  
Silicon Valley ◽  
Communication Technologies ◽  
Multiple Time ◽  
Rapid Rise ◽  
Time Points ◽  
Start Up ◽  
Information And Communication ◽  
Multiple Time Points ◽  
Start Ups

This chapter acknowledges that, for many regions, the idea of attracting cutting-edge tech start-ups is almost irresistible. Seemingly every community aspires to become the next Silicon Valley. But is that feasible? This chapter make these lessons concrete by elaborating on the rapid rise and, even faster and deeper, decline of America’s first Silicon Valley—Cleveland, Ohio. It then shows the near impossibility of trying to become the next Silicon Valley by analyzing the mysterious failure of Atlanta, Georgia—a city that diligently followed all the advice ever given to an aspiring new start-up hub, but somehow was always left only with the “potential.” We will see how at multiple time-points Atlanta’s companies were the leading innovators with the best products in the newest information and communication technologies (ICT), only to falter and be taken over by Silicon Valley companies without leaving any apparent impact on the region. It then brings in social-network research and the concept of embeddedness to explain why trying to recreate a Silicon Valley is a doomed (and expensive) enterprise.

scholarly journals Bayesian splines versus fractional polynomials in network meta-analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Andreas Heinecke ◽  
Marta Tallarita ◽  
Maria De Iorio
Keyword(s):  
Meta Analysis ◽  
Indirect Comparison ◽  
Real Data ◽  
Multiple Time ◽  
Computationally Efficient ◽  
Fractional Polynomials ◽  
Time Points ◽  
Model Based ◽  
Multiple Treatments ◽  
Indirect Comparisons

Abstract Background Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA is becoming increasingly popular in the medical literature and underlying statistical methodologies are evolving both in the frequentist and Bayesian framework. Traditional NMA models are often based on the comparison of two treatment arms per study. These individual studies may measure outcomes at multiple time points that are not necessarily homogeneous across studies. Methods In this article we present a Bayesian model based on B-splines for the simultaneous analysis of outcomes across time points, that allows for indirect comparison of treatments across different longitudinal studies. Results We illustrate the proposed approach in simulations as well as on real data examples available in the literature and compare it with a model based on P-splines and one based on fractional polynomials, showing that our approach is flexible and overcomes the limitations of the latter. Conclusions The proposed approach is computationally efficient and able to accommodate a large class of temporal treatment effect patterns, allowing for direct and indirect comparisons of widely varying shapes of longitudinal profiles.

scholarly journals Image Analysis and Functional Data Clustering for Random Shape Aggregate Models

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1903
Author(s):  
Jonghyun Yun ◽  
Sanggoo Kang ◽  
Amin Darabnoush Tehrani ◽  
Suyun Ham
Keyword(s):  
Image Analysis ◽  
Mixture Model ◽  
Functional Data ◽  
Data Clustering ◽  
Learning Algorithm ◽  
Morphological Characteristics ◽  
Random Shape ◽  
Aggregate Model ◽  
Random Aggregate ◽  
Aggregate Morphology

This study presents a random shape aggregate model by establishing a functional mixture model for images of aggregate shapes. The mesoscale simulation to consider heterogeneous properties concrete is the highly cost- and time-effective method to predict the mechanical behavior of the concrete. Due to the significance of the design of the mesoscale concrete model, the shape of the aggregate is the most important parameter to obtain a reliable simulation result. We propose image analysis and functional data clustering for random shape aggregate models (IFAM). This novel technique learns the morphological characteristics of aggregates using images of real aggregates as inputs. IFAM provides random aggregates across a broad range of heterogeneous shapes using samples drawn from the estimated functional mixture model as outputs. Our learning algorithm is fully automated and allows flexible learning of the complex characteristics. Therefore, unlike similar studies, IFAM does not require users to perform time-consuming tuning on their model to provide realistic aggregate morphology. Using comparative studies, we demonstrate the random aggregate structures constructed by IFAM achieve close similarities to real aggregates in an inhomogeneous concrete medium. Thanks to our fully data-driven method, users can choose their own libraries of real aggregates for the training of the model and generate random aggregates with high similarities to the target libraries.

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