Time-varying Measures Research Articles

Atlantic Trade and Conflict in Europe

Abstract We use over 200 years of conflict and wheat price data to provide the first quantitative evidence that Atlantic trade reduced conflict in Europe. While other determinants of intra-European conflict during the Early to Late Modern period have been proposed, the role of Atlantic trade has not been previously explored due to a lack of historical trade data. We overcome this constraint by using wheat prices to calculate time-varying measures of price pass-through between Europe and the New World, which we use as a proxy for Atlantic trade. To identify the causal effects of Atlantic trade, we exploit exogenous changes in wind patterns and tropical cyclone activity over the Atlantic Ocean to instrument trade. Our results suggest that a one standard deviation increase in Atlantic trade resulted in a 0.157 standard deviation decrease in intra-European conflict onset. We also find empirical support for Atlantic trade increasing real wages and reducing military sizes in Europe, which is consistent with the idea that the possibility of forgone Atlantic trade acted as a deterrent to conflict.

Nonsingular Terminal Sliding Mode Control for Vehicular Platoon Systems with Measurement Delays and Noise

Platooning of vehicular systems has been considered an effective solution for alleviating traffic congestion and reducing energy consumption. Because of limitations in onboard sensors, the measurement system inevitably suffers from measurement delays and noise, yet it receives insufficient attention. In this article, to deal with the measurement delays and noise while improving convergence performance, the platoon control problem of vehicular systems is studied under the nonsingular terminal sliding mode control (NTSMC) framework. A sliding mode observer (SMO) is proposed to estimate the states affected by measurement delays and noise. A distributed NTSMC scheme is developed for the platooning of the vehicular systems and ensures the convergence of the sliding mode surface affected by measurement delays and noise. One salient feature of the proposed SMO is that it can handle time-varying measurement delays rather than constant ones. Moreover, the control law is free of initial spacing error conditions under the employed coupled spacing policy. Numerical simulations are finally provided to demonstrate the effectiveness and efficiency of the proposed algorithm.

An Efficient Attentive-GRU Structure for Uncertainty Modelling of Crowdsourced Human Trajectories Under Building-obscured Urban Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of human mobility analysis and urban navigation, that can affect the performance of human behaviour modelling and location information acquisition. Existing uncertainty modelling algorithms towards the human movement trajectory are subjected to random and highly dynamic human motion characteristics and sampling and observation errors of Global Navigation Satellite System (GNSS) signals caused by the occlusion of buildings in urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of uncertainty modelling. To fill in this gap, this paper proposes an efficient attentive-GRU structure for uncertainty modelling of crowdsourced human trajectories under building-obscured urban scenes, that takes into account both temporal correlation and spatial correlation of human-originated GNSS trajectories and related motion features. A period of human motion data is modelled instead of only one or adjacent location points to avoid interference factors caused by the obstruction of urban buildings, and time-varying measurement and sampling errors are further estimated and combined with comprehensive human motion features to improve the accuracy of final uncertainty modelling. Comprehensive experiments indicate that compared with existing uncertainty modelling methods including physical models and deep-learning models, the proposed attentive-GRU structure realizes much better performance under different accuracy indexes.

Estimation of the graft failure by current value joint model, and extension to alternative parameterization structures: Cohort study.

In clinical practice, individuals are followed up to predict the outcome event of interest, and their longitudinal measurements are collected on a regular or irregular basis. We aimed to examine the classical approach, joint model (JM), and alternative parameterization structures using data on the effect of time-varying longitudinal measurements on survival. The motivating cohort dataset included 158 consecutive kidney transplant recipients who had baseline and follow-up data. Although the longitudinal log-transformed estimated glomerular filtration rate (log[eGFR]) measurements and graft failure have an association clinically, the 2 processes are analyzed separately in the classical approach. In addition to the extended Cox model, the current value JM, the weighted cumulative effect JM, and dynamic predictions were performed in the study, by taking advantage of R codes. Of the 158 patients, 34.8% were males. The mean age was 29.8 ± 10.9 years, and the median age was 26 years at the time of transplantation. The hazard ratio for graft failure was 8.80 for a 1-unit decrease in log(eGFR) in the extended Cox model, 10.58 in the current value JM, and 3.65 in the weighted cumulative effect JM. The presence of coronary heart disease was also found to be associated with log(eGFR): 0.199 (P = .03) for the current value JM and 0.197 (P = .03) for the weighted cumulative effect JM. The current value JM was identified as a better model than the extended Cox model and the weighted cumulative effect JM based on parameter and standard error comparison and goodness of fit criteria. JMs should be preferred, as they facilitate better clinical decisions by accounting for the varying slopes and longitudinal variation of estimated glomerular filtration rate among patients. Suitable types of models should be practiced depending on baseline biomarker levels, their trends over time, the distribution of the biomarkers, and the number of longitudinal biomarkers.

Impact of increasing workforce racial diversity on black-white disparities in cardiovascular disease mortality

BackgroundStructural racism’s influence on workforce policies and practices presents possible upstream targets for assessing and reducing racial health disparities. This study is the first to examine workforce racial diversity in...

Longitudinal association between dentition status and gait speed among older Brazilian adults: SABE cohort study

BackgroundOral health has been associated with general health conditions, but few longitudinal studies evaluated the effect of dentition status on gait speed.ObjectiveThis study aimed to investigate the longitudinal association between different time-varying measures of dentition status (i.e., number of teeth, the presence of periodontal pockets and the functional impact of oral health) and gait speed (outcome) in older Brazilian adults.Materials and methodsThis was a prospective study using data from the Health, Well-being and Aging cohort study (SABE) from 2006, 2010 and 2015. The gait speed was the dependent variable and the independent variables of interest were dentition status evaluated using the number of teeth, use of dental prostheses, presence of periodontal pocket, clinical attachment loss and self-perceived poor functional oral health. Dentition status measures were obtained through clinical oral examinations, performed by trained dentists using standardized criteria proposed by the World Health Organization. Self-perceived poor functional oral health was evaluated using the functional domain of the Geriatric Oral Health Assessment Index. The longitudinal effect of dentition status on gait speed was evaluated using mixed-effects linear models. The effect of the number of teeth/periodontal pocket/attachment loss on gait speed change over time was evaluated by including an interaction term between these variables. The effect of periodontal pocket was tested only among dentate individuals.ResultsData for the complete sample included 3,306 observations from 1,964 individuals. The analyses for dentate individuals included 1,883 observations from 1,149 individuals. There was a positive association between the number of teeth and mean gait speed. Individuals using dental prostheses also had higher means of gait speed than those without dental prostheses. Gait speed was lower among individuals with periodontal pockets and with attachment loss. No interaction was found between any of the indicators of dentition status and time.ConclusionGait speed was associated with dentition status and this association was constant over time.

The minimum-frequency property of complex trace analysis

A waveform has the property of minimum frequency if its instantaneous frequency equals the Hilbert transform of its instantaneous bandwidth plus the spectral low-cut frequency. Minimum frequency is characterized by an amplitude spectrum that is maximally skewed toward the low frequencies. The associated property of maximum frequency is characterized by an amplitude spectrum that is maximally skewed toward the high frequencies. Minimum and maximum frequency are the time-domain counterparts of the spectral properties of minimum and maximum delay. Many waveforms have the property of minimum frequency, such as the Ricker wavelet. Waveforms that have the property of maximum frequency include Klauder wavelets that emphasize high frequencies. Impulsive wavelets can have both minimum frequency and minimum delay. The properties of minimum and maximum frequency suggest time-variant measures to estimate the spectral low-cut and high-cut frequencies of seismic traces and produce an instantaneous frequency attribute that is free of spikes. The property of minimum frequency has long been known but remains unfamiliar to geophysics. It is explained in the context of complex trace analysis illustrated with seismic wavelets and traces.

Time-varying risk aversion and international stock returns

We estimate aggregate, time-varying risk aversion inferred from options, stock returns and macroeconomic data for a panel of 8 countries. We document that, for most countries, the estimated risk aversion measure is counter-cyclical. Moreover, we show that estimated risk aversion forecasts monthly stock index returns up to 12 months ahead. This effect is statistically significant in panel regressions, and it survives the inclusion of additional control variables, such as an estimated of the variance risk premium, an investors’ sentiment index, and a measure of economic uncertainty. Finally, we show that risk aversion provides useful information to an investor who aims at timing the market. An investment strategy that uses the estimated time-varying risk aversion measure to solve a mean–variance asset allocation problem, delivers significantly positive returns.

A variational Bayesian based robust filter for unknown measurement bias and inaccurate noise statistics

In many practical fields, the unknown time-varying measurement biases (additive and multiplicative bias) and heavy-tailed measurement noise caused by some unpredictable anomalous behaviors may degrade the performance of conventional Kalman filter seriously. To solve the state estimation problem of systems with time-varying measurement biases and heavy-tailed measurement noise, this paper proposes a new variational Bayesian (VB) based robust filter. Firstly, the non-Gaussian measurement likelihood probability density function (ML-PDF) with multiplicative and additive measurement bias is built. Then, the conjugate prior distributions for unknown bias and noise scale parameters are selected, and the VB method is utilized to jointly infer the system state, unknown measurement biases and inaccurate measurement noise covariance matrix. Finally, a VB based robust filter is derived and its effectiveness is verified by the numerical simulations.

Neighborhood-Level Nitrogen Dioxide Inequalities Contribute to Surface Ozone Variability in Houston, Texas.

In Houston, Texas, nitrogen dioxide (NO2) air pollution disproportionately affects Black, Latinx, and Asian communities, and high ozone (O3) days are frequent. There is limited knowledge of how NO2 inequalities vary in urban air quality contexts, in part from the lack of time-varying neighborhood-level NO2 measurements. First, we demonstrate that daily TROPOspheric Monitoring Instrument (TROPOMI) NO2 tropospheric vertical column densities (TVCDs) resolve a major portion of census tract-scale NO2 inequalities in Houston, comparing NO2 inequalities based on TROPOMI TVCDs and spatiotemporally coincident airborne remote sensing (250 m × 560 m) from the NASA TRacking Aerosol Convection ExpeRiment-Air Quality (TRACER-AQ). We further evaluate the application of daily TROPOMI TVCDs to census tract-scale NO2 inequalities (May 2018-November 2022). This includes explaining differences between mean daily NO2 inequalities and those based on TVCDs oversampled to 0.01° × 0.01° and showing daily NO2 column-surface relationships weaken as a function of observation separation distance. Second, census tract-scale NO2 inequalities, city-wide high O3, and mesoscale airflows are found to covary using principal component and cluster analysis. A generalized additive model of O3 mixing ratios versus NO2 inequalities reproduces established nonlinear relationships between O3 production and NO2 concentrations, providing observational evidence that neighborhood-level NO2 inequalities and O3 are coupled. Consequently, emissions controls specifically in Black, Latinx, and Asian communities will have co-benefits, reducing both NO2 disparities and high O3 days city wide.

Trust predicts compliance with COVID-19 containment policies: Evidence from ten countries using big data

We use Twitter, Google mobility, and Oxford policy data to study the relationship between trust and compliance over the period March 2020 to January 2021 in ten, mostly European, countries. Trust has been shown to be an important correlate of compliance with COVID-19 containment policies. However, the previous findings depend upon two assumptions: first, that compliance is time invariant, and second, that compliance can be measured using self reports or mobility measures alone. We relax these assumptions by calculating a new time-varying measure of compliance as the association between containment policies and people's mobility behavior. Additionally, we develop measures of trust in others and national institutions by applying emotion analysis to Twitter data. Results from various panel estimation techniques demonstrate that compliance changes over time and that increasing (decreasing) trust in others predicts increasing (decreasing) compliance. This evidence indicates that compliance changes over time, and further confirms the importance of cultivating trust in others.

Imbalanced temporal states of cortical blood-oxygen-level-dependent signal variability during rest in episodic migraine

BackgroundMigraine has been associated with functional brain changes including altered connectivity and activity both during and between headache attacks. Recent studies established that the variability of the blood-oxygen-level-dependent (BOLD) signal is an important attribute of brain activity, which has so far been understudied in migraine. In this study, we investigate how time-varying measures of BOLD variability change interictally in episodic migraine patients.MethodsTwo independent resting state functional MRI datasets acquired on 3T (discovery cohort) and 1.5T MRI scanners (replication cohort) including 99 episodic migraine patients (n3T = 42, n1.5T=57) and 78 healthy controls (n3T = 46, n1.5T=32) were analyzed in this cross-sectional study. A framework using time-varying measures of BOLD variability was applied to derive BOLD variability states. Descriptors of BOLD variability states such as dwell time and fractional occupancy were calculated, then compared between migraine patients and healthy controls using Mann-Whitney U-tests. Spearman’s rank correlation was calculated to test associations with clinical parameters.ResultsResting-state activity was characterized by states of high and low BOLD signal variability. Migraine patients in the discovery cohort spent more time in the low variability state (mean dwell time: p = 0.014, median dwell time: p = 0.022, maximum dwell time: p = 0.013, fractional occupancy: p = 0.013) and less time in the high variability state (mean dwell time: p = 0.021, median dwell time: p = 0.021, maximum dwell time: p = 0.025, fractional occupancy: p = 0.013). Higher uptime of the low variability state was associated with greater disability as measured by MIDAS scores (maximum dwell time: R = 0.45, p = 0.007; fractional occupancy: R = 0.36, p = 0.035). Similar results were observed in the replication cohort.ConclusionEpisodic migraine patients spend more time in a state of low BOLD variability during rest in headache-free periods, which is associated with greater disability. BOLD variability states show potential as a replicable functional imaging marker in episodic migraine.

Is gold a preferable diversifier of cleaner equity risk across diverse scenarios? Evidence from multidimensional connectedness and spillover measures

The study aims to explore the risk management potential of historical (gold and crude oil) and modern (bitcoin) assets against the shocks in clean energy stocks. Specifically, the quest related to which asset(s)? In what proportion? In which market condition? and for what time preference? should be held with cleaner equity. The exploration is based on the presumptions that conventional and cleaner stocks are alike and that financial markets (assets) have an asymmetric interdependence. We also assume that the interdependence among the considered markets is driven by various events over time and distinct investment horizons. To that end, we employ a generalized vector autoregressive-based overall, time-varying, asymmetric, and frequency-based connectedness measures and DCC-GARCH model to the daily data ranging from October 15, 2014 to April 12, 2023. The spillovers across the considered markets confirm the similarity of conventional and cleaner stocks and the asymmetry hypothesis, suggesting that downside shock transmission (bad spillovers) outperforms upside shock transmission (good spillovers). The time-varying results suggest that connectedness among the considered markets is higher, and asymmetric during Brexit-2016, the COVID-19 pandemic, Russia's invasion of Ukraine in early 2022, and the monetary policy tightening of 2022 and 2023. Finally, we show that gold is a preferable diversifier of cleaner equity risk on average, during distress events, downside markets, and across all investment horizons. The study has implications for financial market participants to strategically manage the cleaner equity risk with gold in diverse market scenarios.

Does firm-level teamwork culture affect cash holdings?

PurposePrior literature on the cultural determinants of cash holdings focuses on time-invariant cultural variables measured at the geographical level. These measures of culture do not capture the firm-level variation in corporate culture. Using a validated time-variant measure of firm-level corporate culture, specifically teamwork, we examine the effect of teamwork on a firm's cash holdings.Design/methodology/approachIn this paper, we explore the effect of corporate teamwork culture on firms' cash holdings. Using sudden CEO turnover as an exogenous shock to a firm's teamwork culture, we find teamwork increases cash holdings. Also, we test and find two channels—financial constraint and agency—and two new labour-related channels—human capital quality and labour inefficiency—through which teamwork culture affects cash holdings. Our results are robust to endogeneity tests.FindingsWe find that teamwork increases the cash holdings of firms. We find that a firm with a high teamwork culture has higher cash holdings: an increase of one standard deviation in teamwork leads to a 14.6% rise in the mean cash holdings.Originality/valueTo our knowledge, our study is the first to introduce the firm-level teamwork cultural construct as a determinant of cash holdings.

Robust UKF orbit determination method with time-varying forgetting factor for angle/range-based integrated navigation system

The angle/range-based integrated navigation system is a favorable navigation solution for deep space explorers. However, the statistical characteristics of the measurement noise are time-varying, leading to inaccuracies in the derived measurement covariance even causing filter divergence. To reduce the gap between theoretical and actual covariances, some adaptive methods use empirically determined and unchanged forgetting factors to scale innovations within the sliding window. However, the constant weighting sequence cannot accurately adapt to the time-varying measurement noise in dynamic processes. Therefore, this paper proposes an Adaptive Robust Unscented Kalman Filter with Time-varying forgetting factors (TFF-ARUKF) for the angle/range integrated navigation system. Firstly, based on a statistically linear regression model approximating the nonlinear measurement model, the M-estimator is adopted to suppress the interference of outliers. Secondly, the covariance matching method is combined with the Huber linear regression problem to adaptively adjust the measurement noise covariance used in the M-estimation. Thirdly, to capture the time-varying characteristics of the measurement noise in each estimation, a new time-varying forgetting factors selection strategy is designed to dynamically adjust the adaptive matrix used in the covariance matching method. Simulations and experimental analysis compared with EKF, AMUKF, ARUKF, and Student’s t-based methods have validated the effectiveness and robustness of the proposed algorithm.

Sovereign risk dynamics in the EU: The time varying relevance of fiscal and external (im)balances*

Acknowledging the potential detrimental impact that twin-deficits can have on sovereign risk, this study uses a two-step approach to assess the impact of fiscal and external sustainability on sovereign risk dynamics for a panel of 27 European economies from Q4 2001 to Q3 2022. We first estimate a country-specific time-varying measure of fiscal sustainability, based on the cointegration between government revenues and expenditures and external sustainability, derived from the cointegration of exports and imports. We then use these time-varying coefficients to assess their impact on sovereign risk, proxied by 10-year CDS and CDS spreads (against the US), employing a Weighted Least Squares (WLS) analysis. Notably, we show that an improvement of both fiscal and external sustainability lead to a reduction in sovereign risk. This phenomenon is particularly pronounced for countries experiencing an upward trajectory in public debt levels.

Tracking time-varying brand equity using household panel data

This paper proposes a novel yet simple approach for tracking time-varying utility-based brand equity using household panel data. Our two-stage procedure allows us to estimate a discrete choice model that explicitly accounts for consumer heterogeneity, state dependence, price endogeneity, and dynamic brand equity. We present dynamic brand utility as a time-varying measure that adds to current market-based or mindset metrics. An empirical study of household purchases in the detergent category reveals that brand equity varies over time. The temporal changes in the brand equity of single brands and the differences across brands have significant implications for consumers’ brand choice and, consequently, for brand management. Therefore, the dynamic utility-based model offers valuable support for tracking a brand’s state, including the brand’s competition, and for making appropriate marketing decisions.

Association of triglyceride-glucose index and its related parameters with atherosclerotic cardiovascular disease: evidence from a 15-year follow-up of Kailuan cohort

BackgroundTriglyceride glucose (TyG) index and its related parameters have been introduced as cost-effective surrogate indicators of insulin resistance, while prospective evidence of their effects on atherosclerotic cardiovascular disease (ASCVD) remained scattered and inconsistent. We aimed to evaluate the association of TyG and its related parameters with new-onset ASCVD, and the predictive capacity were further compared.MethodA total of 95,342 ASCVD-free participants were enrolled from the Kailuan study. TyG and its related parameters were defined by fasting blood glucose, triglyceride, body mass index (BMI), waist circumstance (WC) and waist-to-height ratio (WHtR). The primary outcome was incident ASCVD, comprising myocardial infarction (MI) and ischemic stroke (IS). Cox proportional hazard models and restricted cubic spline (RCS) analyses were adopted to investigate the association between each index and ASCVD. The C-index, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) were used for comparison of their predictive value for ASCVD.ResultsDuring a median follow-up of 15.0 years, 8,031 new cases of ASCVD were identified. The incidence rate of ASCVD increased along with elevated levels of each index, and the relationships were found to be nonlinear in the RCS analyses. The hazard ratio (HR) and 95% confidence interval (95% CI) for ASCVD was 1.39 (1.35, 1.43), 1.46 (1.41, 1.50), 1.50 (1.46, 1.55), and 1.52 (1.48, 1.57) per 1 IQR increase of baseline TyG, TyG-BMI, TyG-WC, and TyG-WHtR, respectively, and the association were more pronounced for females and younger individuals aged < 60 years (Pfor interaction<0.05). Using the updated mean or time-varying measurements instead of baseline indicators did not significantly alter the primary findings. Additionally, TyG-WC and TyG-WHtR showed better performance in predicting risk of ASCVD than TyG, with the IDI (95% CI) of 0.004 (0.001, 0.004) and 0.004 (0.001, 0.004) and the category-free NRI (95% CI) of 0.120 (0.025, 0.138) and 0.143 (0.032, 0.166), respectively. Similar findings were observed for MI and IS.ConclusionsBoth the TyG index and its related parameters were significantly and positively associated with ASCVD. TyG-WC and TyG-WHtR had better performance in predicting incident ASCVD than TyG, which might be more suitable indices for risk stratification and enhance the primary prevention of ASCVD.

Visual Interactive Parameter Selection for Temporal Blind Source Separation

Many fields of science and industry collect and analyze multivariate time-varying measurements, e.g., healthcare, geophysics, or finance. Such data is often high-dimensional, correlated, and noisy. Experts are interested in latent components of the dataset, but due to the aforementioned properties these are difficult to obtain. Temporal Blind Source Separation (TBSS) is a suitable and well-established framework for these data. However, the large choice of methods and their tuning parameters impedes the effective use of TBSS in practice. The goal of Visual Analytics (VA) is to create powerful analytic tools by combining the strengths of humans and computers. We designed, developed, and evaluated VA contributions in previous work to support TBSS-related analysis tasks. In this paper, we highlight the benefits and opportunities of VA concepts for statistic-oriented problems using a real-world TBSS application example with a dataset of climate and meteorological measurements in Italy.

Uncontrolled diabetes mellitus and the progression of monoclonal gammopathy of undetermined significance to multiple myeloma in US veterans.

7551 Background: Studies have shown that, independent of obesity, the metabolic effects of diabetes mellitus (DM) promote the progression of monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM). However, it is unclear whether glycemic control is associated with MGUS progression in DM patients. We aimed to evaluate the association between poor glycemic control, measured by HbA1c &gt;6.5%, and progression. Methods: Patients diagnosed with MGUS from 1999-2021 in the Veterans Health Administration (VHA) were identified using a published natural language processing (NLP)-based algorithm. We focused on patients whose MGUS subtype was IgG, IgA, or light chain. We excluded patients lacking body weight measurements from the time of MGUS diagnosis to the end of follow-up to ensure receiving care in the VHA. We further excluded patients with extended gaps &gt;1 year between HbA1c measurements. Time-varying HbA1c measurements during the follow-up, evaluated at 1-day intervals and categorized into uncontrolled (HbA1c &gt;6.5%) or controlled (≤6.5%) DM was the exposure variable. Progression of MGUS to MM was also confirmed by a published NLP algorithm. The association between the time-varying controlled/uncontrolled DM and progression was estimated by multivariable-adjusted hazard ratio (aHR) with death as a competing event. The covariates included sex, race, MGUS subtype as well as age, body mass index, monoclonal protein level, Charlson Comorbidity Index, DM medication, and DM type (type 1, type 2 with insulin-dependency status, type 2 without insulin-dependency status), all at MGUS diagnosis. Same analysis was conducted in a subgroup of patients with non-insulin dependent DM type 2 (NIDDMT2) without insulin use between MGUS diagnosis and the end of follow-up. Results: After applying inclusion and exclusion criteria, we identified 9,682 patients with both DM and MGUS. The multivariable analysis revealed a positive association between poor glycemic control and MM progression (see Table): (aHR 1.27, 95% CI 1.13-1.42). In NIDDMT2 without insulin use (n=3,498), poor glycemic control was associated with MM progression (aHR 1.81; 95% CI 1.57-2.10). Conclusions: For patients with DM and MGUS, the risk of MGUS progression to MM was 27% higher in uncontrolled DM and 81% higher in patients with NIDDMT2. This finding highlights a modifiable risk factor for MM progression in MGUS patients. Future studies should examine if interventions to enhance glycemic control could potentially reduce MGUS progression.