Time-varying Model Research Articles

Online monitoring of profiles via RVC model with an application to industrial busbar

ABSTRACT Profile monitoring is used to check the stability of the functional relationship of the process response on several predictors. In most of the existing literature, the monitored functional relationship usually involves only one covariate and the coefficient of the covariate is assumed to be constant. In some applications, the process response depends on multiple covariates, and the functional relationship is not constant but dynamic over time, which is different from those studied in the literature. To implement profile monitoring in these applications, a semiparametric random time-varying coefficient model is employed to characterize this dynamic relationship over time. Based on this model, a profile monitoring scheme integrated with dynamic probability control limits is proposed, which can adapt to the case of the within-profile autocorrelation and arbitrary design points. In Phase I, this paper uses a backfitting iterative procedure with the REML-Based EM-Algorithm to estimate the model. In Phase II, an exponentially weighted moving average scheme on residuals with dynamic probability control limits is developed. The simulation results show that the proposed scheme performs better in many scenarios. Finally, an application to industrial busbar running process monitoring is given to illustrate the scheme’s implementation.

Long-term exposure to ambient particulate matter is associated with prognosis in people living with HIV/AIDS: Evidence from a longitudinal study

BackgroundEvidence on the association between particulate matter (PM) exposure and prognosis in people living with HIV/AIDS (PWHA) is scarce. We aim to investigate the associations of long-term exposure to PM with AIDS-related deaths and complications. MethodsWe collected follow-up information on 7444 PWHAs from 2000 to 2021 from the HIV/AIDS Comprehensive Response Information Management System of the Wuhan Center for Disease Control and Prevention. The AIDS-related deaths and complications were assessed by physicians every 3 to 6 months, and the monthly average PM concentrations for each PWHA were extracted from the China High Air Pollutants dataset. We employed time-varying Cox regression models to evaluate the associations of the average cumulative PM exposure concentrations with AIDS-related deaths and complications, as well as the mediating effects of AIDS-related complications in PM-induced AIDS-related deaths. ResultsFor each 1 μg/m3 increase in PM1, PM2.5, and PM10, the adjusted hazard ratios (HRs) for AIDS-related deaths were 1.021 (1.009, 1.033), 1.012 (1.005, 1.020), and 1.010 (1.005, 1.015), respectively; and the HRs for AIDS-related complications were 1.049 (1.034, 1.064), 1.029 (1.020, 1.038), and 1.031 (1.024, 1.037), respectively. AIDS-related complications mediated 18.38 % and 18.68 % of the association of exposure to PM1 and PM2.5 with AIDS-related deaths, respectively. The association of PM exposure with AIDS-related deaths was more significant in older PWHA. Meanwhile, the association between PM exposure and AIDS-related complications was stronger in PWHA with a BMI ≥ 24 kg/m2. ConclusionLong-term exposure to PM is positively associated with AIDS-related deaths and complications, and AIDS-related complications have mediating effects in PM-induced AIDS-related deaths. Our evidence emphasizes that enhanced protection against PM exposure for PWHAs is an additional mitigation strategy to reduce AIDS-related deaths and complications.

Dynamic connectedness among market volatilities: a perspective of COVID-19 and Russia-Ukraine conflict

PurposeThis paper aims to investigate the dynamic volatility connectedness among 13 G20 countries by using the volatility indices.Design/methodology/approachThe connectedness approach based on the time-varying parameter vector autoregression model has been used to investigate the linkage. The period of study is from 1 January 2014 to 20 April 2023.FindingsThis analysis revealed that volatility connectedness among the countries during COVID-19 and Russia–Ukraine conflict had increased significantly. Furthermore, analysis has indicated that investors had not anticipated the World Health Organization announcement of COVID-19 as a global pandemic. Contrarily, investors had anticipated the Russian invasion of Ukraine, evident in a significant rise in volatility before and after the invasion. In addition, the transmission of volatility is from developed to developing countries. Developed countries are NET volatility transmitters, whereas developing countries are NET volatility receivers. Finally, the ordinary least square regression result suggests that the volatility connectedness index is informative of stock market dynamics.Originality/valueThe connectedness approach has been widely used to estimate the dynamic connectedness among market indices, cryptocurrencies, sectoral indices, enegy commodities and metals. To the best of the authors’ knowledge, none of the previous studies have directly used the volatility indices to measure the volatility connectedness. Hence, this study is the first of its kind that has used volatility indices to measure the volatility connectedness among the countries.

Data-driven replay attack detection for unknown cyber-physical systems

This paper considers a data-driven method to detect stealthy replay attacks on cyber-physical systems (CPSs) whose models are unknown. Most existing attack detection methods assume the system dynamic is known. However, due to the large scale and complexity of the system, it is difficult to get an accurate model of a CPS. This paper uses a moving window subspace identification method to construct a linear discrete time-varying model of CPS. The controllability and observability of the obtained model are proved. On this basis, the replay attack is transformed into a detectable additive attack using the output coding strategy. Then, considering modeling errors, a time-varying H∞ filter is designed, and a detection function based on the output residual is constructed. When the system works without any attacks, the detection function approaches a limited threshold. On the contrary, that function will increase extremely larger than the threshold when the replay attack occurs. The detectability of our scheme for attacks is also mathematically proven. Finally, to verify the effectiveness of the proposed scheme, we provide simulation results on a linear motor system and a nonlinear robotic system.

Identifying and Interpreting Hydrological Model Structural Nonstationarity Using the Bayesian Model Averaging Method

Understanding hydrological nonstationarity under climate change is important for runoff prediction and it enables more robust decisions. Regarding the multiple structural hypotheses, this study aims to identify and interpret hydrological structural nonstationarity using the Bayesian Model Averaging (BMA) method by (i) constructing a nonstationary model through the Bayesian weighted averaging of two lumped conceptual rainfall–runoff (RR) models (the Xinanjiang and GR4J model) with time-varying weights; and (ii) detecting the temporal variation in the optimized Bayesian weights under climate change conditions. By combining the BMA method with period partition and time sliding windows, the efficacy of adopting time-varying model structures is investigated over three basins located in the U.S. and Australia. The results show that (i) the nonstationary ensemble-averaged model with time-varying weights surpasses both individual models and the ensemble-averaged model with time-invariant weights, improving NSE[Q] from 0.04 to 0.15; (ii) the optimized weights of Xinanjiang model increase and that of GR4J declines with larger precipitation, and vice versa; (iii) the change in the optimized weights is proportional to that of precipitation under monotonic climate change, as otherwise the mechanism changes significantly. Overall, it is recommended to adopt nonstationary structures in hydrological modeling.

Forecasting carbon prices under diversified attention: A dynamic model averaging approach with common factors

To improve the predictability of carbon prices under diversified attention, this paper develops a dynamic model averaging approach with common factors (DMA-CF) which uses dimension-reduction techniques to extract factors from all models including the subsets of attention predictors and allows time-varying coefficients and model switching. The in-sample results using univariate models reveal the strong predictive power of the attention measured by the Google search volume index (AGSVI). The out-of-sample results confirm it as the strongest attention predictor, and show the superior performance of DMA-CFs relative to the original DMA and the benchmark. We further investigate how common factors improve the forecasting performance of DMA. The empirical evidence indicates that common factors efficiently aggregate the information and reduce the estimation errors in complicated models which are assigned with higher probability in DMA-CFs than in DMA, thereby digging out more predictive information for forecasting carbon prices. Moreover, DMA primarily depends on AGSVI, with the highest weights, while DMA-CFs slightly downweight the AGSVI and allocate more weight to the attention proxy of abnormal trading volume (AAVol) which provides complementary information. Finally, our DMA-CF methods can improve the economic gains in the portfolio exercise with carbon futures.

What time-varying network models based on functional analysis tell us about the course of a patient's problem

Background: Relations among psychological variables are assumed to be complex and to vary over time. Personalized networks can model multivariate complex interactions. The development of time-varying networks allows to model the variation of parameters over time. Objectives: We aimed to determine the value of time-varying networks for clinical practice. Methods: We applied time-varying mixed graphical models (TV-MGM) and time-varying vector autoregressive models (TV-VAR) to intensive longitudinal data of nine participants with depressive symptoms (n = 6) or anxiety (n = 3). Results: Most of the participants showed temporal changes in network topology within the assessment period of 30 days. Time-varying networks of participants with small, medium, and large time variability in edge parameters clearly show the different temporal evolvements of dynamic interactions between variables. The case example indicates clinical utility but also limitations to the application of time-varying networks in clinical practice. Conclusion: Time-varying network models provide a data-driven and exploratory approach that could complement current diagnostic standards by reflecting interacting, often mutually reinforcing processes of mental health problems and by accounting for variation over time. They can be used to generate hypotheses for further confirmatory and clinical testing.

Time-varying reliability models for parallel systems consisting of beam structures with crack flaws

Abstract. Time-varying reliability assessment of parallel systems of beam structures with crack defects is of great importance for structural safety evaluation. In this paper, time-varying reliability models for parallel systems with cracked beams are proposed. In the modeling process, the statistical correlation between crack depth, random working load, random strength degradation, and random stress response as well as the complex failure correlation jointly caused by these factors are considered. Based on the finite element analysis of the cracked beams, the reliability models are constructed by combining the neural network and the response surface agent model. The validity of the model is verified by the Monte Carlo simulation method. The results show that crack depth and working load dispersion have important effects on reliability and failure correlation.

Greater haemodialysis exposure (‘quotidian haemodialysis’) has different mortality associations by patient age group

ABSTRACT Background Worldwide, most people requiring kidney replacement therapy receive haemodialysis (HD) three times per week. Greater HD time and/or frequency may improve survival, but implementation requires understanding potential benefits across the range of patients. Methods Using data from the Australia and New Zealand Dialysis and Transplant Registry, we assessed whether quotidian HD (defined as >3 sessions/week and/or >5 h/session) was associated with reduced mortality in adult patients. The primary outcome of all-cause mortality was analysed by a time-varying Cox proportional hazards model with quotidian HD as the exposure of interest. Results Of 24 138 people who received HD between 2011 and 2019, 2632 (10.9%) received quotidian HD at some stage. These patients were younger, more likely male and more likely to receive HD at home. Overall, quotidian versus standard HD was associated with a decreased risk for all-cause mortality {crude hazard ratio [HR] 0.50 [95% confidence interval (CI) 0.45–0.56]}, but an interaction between quotidian HD and age was identified (P = .005). Stratified by age groups and splitting follow-up time where proportional hazards were violated, the corresponding HR compared with standard HD was 2.43 (95% CI 1.56–3.79) for people >75 years of age in the first year of quotidian HD, 1.52 (95% CI 0.89–2.58) for 1–3 years and 0.95 (95% CI 0.51–1.78) for ≥3 years. There was no significant survival advantage in younger people. Conclusions Although quotidian HD conferred survival benefit in crude analyses, people ≥75 years of age had greater mortality with quotidian HD than standard HD. The mortality benefit in younger people was attenuated when adjusted for known confounders.

Consumer and business confidence connectedness in the euro area: a tale of two crises

Purpose The purpose of this study is to examine the propagation of consumer and business confidence in the euro area with a particular focus on the global financial crisis (GFC), the European sovereign debt crisis (ESDC) and the COVID-19-induced Great Lockdown. Design/methodology/approach The authors apply Diebold and Yilmaz’s connectedness framework and the improved method based on the time-varying parameter vector autoregressive model. Findings The authors find that although the evolution of business confidence marked the GFC and the ESDC the role of consumer confidence (mainly in those countries with stricter containment and closure measures) increased in the COVID-19-induced crisis. Originality/value The findings are related to the different origins of the examined crisis periods, and the analysis of their interrelationship is a very relevant topic for future research.

Long-term exposure to ambient fine particulate matter and risk of liver cancer in the NIH-AARP Diet and Health Study

BackgroundFine particulate matter (PM2.5) exposure has been associated with liver cancer incidence and mortality in a limited number of studies. We sought to evaluate this relationship for the first time in a U.S. cohort with historical exposure assessment. MethodsWe used spatiotemporal prediction models to estimate annual average historical PM2.5 concentrations (1980–2015) at residential addresses of 499,729 participants in the NIH-AARP Diet and Health Study, a cohort in 6 states (California, Florida, Louisiana, New Jersey, North Carolina, and Pennsylvania) and 2 metropolitan areas (Atlanta, Georgia, and Detroit, Michigan) enrolled in 1995–1996 and followed up through 2017. We used a time-varying Cox model to estimate the association for liver cancer and the predominant histologic type, hepatocellular carcinoma (HCC), per 5 µg/m3 increase in estimated outdoor PM2.5 levels, incorporating a 5-year average, lagged 10 years prior to cancer diagnosis and adjusting for age, sex, race/ethnicity, education level and catchment state. We also evaluated PM2.5 interactions with hypothesized effect modifiers. ResultsWe observed a non-significantly increased risk of liver cancer associated with estimated PM2.5 exposure (Hazard ratio [HR] = 1.05 [0.96–1.14], N = 1,625); associations were slightly stronger for HCC, (84 % of cases; HR = 1.08 [0.98–1.18]). Participants aged 70 or older at enrollment had an increased risk of liver cancer versus other age groups (HR = 1.50 [1.01–2.23]); p-interaction = 0.01) and risk was elevated among participants who did not exercise (HR = 1.81 [1.22–2.70]; p-interaction = 0.01). We found no evidence of effect modification by sex, smoking status, body mass index, diabetes status, or alcohol consumption (p-interaction > 0.05). ConclusionsOur findings in this large cohort suggest that residential ambient PM2.5 levels may be associated with liver cancer risk. Further exploration of the variation in associations by age and physical activity are important areas for future research.

Locally time-varying parameter regression

. I discuss a framework to allow dynamic sparsity in time-varying parameter regression models. The conditional variances of the innovations of time-varying parameters are time varying and equal to zero adaptively via thresholding. The resulting model allows the dynamics of the time-varying parameters to mix over different frequencies of parameter changes in a data driven way and permits great flexibility while achieving model parsimony. A convenient strategy is discussed to infer if each coefficient is static or dynamic and, if dynamic, how frequent the parameter change is. An MCMC scheme is developed for model estimation. The performance of the proposed approach is illustrated in studies of both simulated and real economic data.

Age-varying associations between attempts to change weight and suicidal ideation among adolescents in the United States

PurposeThe goal of this study was to inform theory and suicide prevention efforts by examining how the link between attempts to change weight and suicidal ideation varies across adolescence, when it is strongest, and whether there are sex differences. MethodsData were from 13,518 youth ages 14–18 years who participated in the 2019 Youth Risk Behavior Survey, a nationally representative study of U.S. high school students. Time-varying effect modeling was used to estimate associations between attempts to change weight and suicidal ideation as continuous functions of age and to test sex differences in these links. ResultsThe link between attempts to lose weight and suicidal ideation was positive and significant between ages 14.0–17.3 years, with the strongest association at age 15.0 (OR = 2.07). There were no sex differences in this age-varying association. The association between attempts to gain weight and suicidal ideation was positive and age-varying for girls, with the strongest association at age 17.2 years (OR = 2.79). This association was not significant for boys at any age. ConclusionsGirls and boys who are trying to lose weight and girls who are trying to gain weight may be at elevated risk for suicidal ideation between ages 14 and 18 years. Findings illuminate patterns of association during adolescence and have implications for suicide prevention.

Advancing Marine-Bearing Capacity and Economic Growth: A Comprehensive Analysis of Blue Economy Resilience, Network Evolution, and Technological Influences in China’s Coastal Areas

This study presents a groundbreaking approach to evaluating the resilience of China’s blue economy, shedding light on its critical role in promoting sustainable development along the nation’s coastlines. By employing advanced methodologies such as social network analysis and the time-varying effect random graph model (TERGM), our research meticulously examines the period from 2007 to 2019. It uncovers the complex dynamics of resilience, focusing on the adversities of unbalanced growth and pinpointing pivotal factors which shape this resilience, including the stability of the marine economy, the rigor of environmental regulations, and the impact of technological progress. Through a strategic compilation of indicators, our analysis offers a detailed perspective on the multi-faceted nature of blue economy resilience. The outcomes underscore the necessity of enhancing network effectiveness and implementing specific measures to encourage sustainable expansion in coastal domains. Leveraging these insights, we advocate for targeted strategies to refine the resilience network’s framework, aiming to bolster the sustainable evolution of marine economic activities. This study not only deepens the understanding of marine economic resilience but also charts a course for achieving a resilient and sustainable blue economy. It stands as an indispensable guide for policymakers and scholars in the realm of marine economics, offering a blueprint for navigating the challenges and opportunities within this vital sector.

Insights into the dynamics of market efficiency spillover of financial assets in different equity markets

This paper provides multidisciplinary insights into the dynamics of market efficiency spillover of financial assets, considering Bitcoin, DXY (U.S. Dollar Index), S&P 500, and crude oil in different equity markets. Given this, we employ the multifractal detrended fluctuation analysis (MF-DFA) and the time-varying parameter vector autoregressive models (TVP-VAR) to illuminate these complex interplays between these assets and other equity markets. Our findings display that Bitcoin acts as a net transmitter, whereas DXY functions as a net receiver. Additionally, Bitcoin, DXY, and S&P 500 clearly exhibit dynamics in information transmission, whereas crude oil does not. It means that the low connectedness of the oil market in terms of market efficiency spillover, suggesting that the oil market may play an effective role as a hedging instrument for other assets.

Robust model-free adaptive iterative learning control for an autonomous bus trajectory tracking system.

A robust model-free adaptive iterative learning control (R-MFAILC) algorithm is proposed in this work to address the issue of laterally controlling an autonomous bus. First, according to the periodic repetitive working characteristics of autonomous buses, a novel dynamic linearized method used in the iterative domain is utilized, and a time-varying data model with a pseudo gradient (PG) is given. Then, the R-MFAILC controller is designed with a proposed adaptive attenuation factor. The proposed algorithm's advantage lies in the R-MFAILC controller, which solely utilizes the input and output data of the regulated entity. Moreover, the R-MFAILC controller has strong robustness and can handle the nonlinear measurement disturbances of the system. In simulations based on the Truck-Sim simulation platform, the effectiveness of the proposed algorithm is verified. A rigorous mathematical analysis is employed to demonstrate the stability and convergence of the proposed algorithm.

Novel method for identifying the stages of discharge underwater based on impedance change characteristic

It is difficult to determine the discharge stages in a fixed time of repetitive discharge underwater due to the arc formation process being susceptible to external environmental influences. This paper proposes a novel underwater discharge stage identification method based on the Strong Tracking Filter (STF) and impedance change characteristics. The time-varying equivalent circuit model of the discharge underwater is established based on the plasma theory analysis of the impedance change characteristics and mechanism of the discharge process. The STF is used to reduce the randomness of the impedance of repeated discharges underwater, and then the universal identification resistance data is obtained. Based on the resistance variation characteristics of the discriminating resistance of the pre-breakdown, main, and oscillatory discharge stages, the threshold values for determining the discharge stage are obtained. These include the threshold values for the resistance variation rate (K) and the moment (t). Experimental and error analysis results demonstrate the efficacy of this innovative method in discharge stage determination, with a maximum mean square deviation of S cr less than 1.761.

Association between sleep-disordered breathing and post-stroke fatigue in patients with ischemic stroke

ObjectivesPost-stroke fatigue (PSF) is common and often disabling. Sleep-disordered breathing (SDB) is highly prevalent among stroke survivors and can cause fatigue. We explored the relationship between SDB and PSF over time. Materials and MethodsIschemic stroke (IS) patients within the BASIC project were offered SDB screening with a well-validated cardiopulmonary sleep apnea test at 0, 3-, 6-, and 12-months post-stroke. The primary exposure was the respiratory event index (REI; sum of apneas plus hypopneas per hour). The primary outcome was PSF, measured by the SF-36 vitality scale. Associations between REI and PSF were evaluated using linear regression including time-by-REI interactions, allowing the effect of REI to vary over time. ResultsOf the 411 IS patients who completed at least one outcome interview, 44 % were female, 61 % Mexican American (MA), 26 % non-Hispanic white, with a mean age of 64 (SD 10). Averaged across timepoints, REI was not associated with PSF. In a time-varying model, higher REI was associated with greater PSF at 3-months (β = 1.75, CI = 0.08, 3.43), but not at 6- or 12-months. Across timepoints, female sex, depressive symptoms, and comorbidity burden were associated with greater PSF, whereas MA ethnicity was associated with less PSF. ConclusionsHigher REI was associated with modestly greater PSF in the early post-stroke period, but no association was observed at 6 months and beyond. SDB may be a modest modifiable risk factor for early PSF, but its treatment is unlikely to have a substantial impact on long-term PSF. MA ethnicity seems to be protective against PSF.

Characteristics of Surface Charge Accumulation on Spacers and Its Influencing Factors

Charge accumulation usually happens on the surface of spacers under DC operation, which is susceptible to inducing surface flashover. In order to explore the surface charge accumulation mechanisms and the influences of dielectric conductivity, gas ion mobility, and temperature field on the surface charges, a time-varying charge density model at the gas–solid interface of spacers was established. The results of the simulation show that the discontinuity of the current density between the spacer bulk side and the gas ion flow is the fundamental reason for the charge accumulation on the spacer surface. Additionally, the value of current density fluxes at the interface continues to decrease with the change of the electric field, and the progress of charge transfer gradually stabilizes. Moreover, the dielectric conductivity directly affects the charge accumulation process, and there is a critical conductivity in which the effect of charge conduction in dielectrics counteracts that of gas-phase charge deposition, theoretically. When the conductivity is higher than the critical conductivity, the solid-side charge conduction is the main source of the surface charge accumulation, while the gas-phase charge deposition on the gas side plays a dominant role when the conductivity is lower than the critical conductivity. The charge accumulation is not significantly affected by gas ion mobility when the temperature is evenly distributed. However, under the temperature field with gradient distribution, the current density fluxes at the interface change, causing the polarity reverse of the accumulated charge. In the high-temperature region, the volume current density surges simultaneously with the conductivity, leading to a higher density of surface charge accumulation. Lastly, the design of spacers needs to keep the current densities on both sides of the interface as similar as possible in order to avoid excessive charge gathering in localized areas.

Natural resource development, economic policy uncertainty and urban residents’ consumption in China: a nonlinear ARDL and time-varying parameter vector autoregressive with stochastic volatility approach

Increased risks of economic policy uncertainty and overexploitation of natural resources exist in China. At the same time, the growth rate of urban residents’ consumption has generally declined. The paper analyses the role of economic policy uncertainty (EPU) and natural resource exploitation on the urban residents’ consumption in China. Based on the data from the first quarter of 2002 to the third quarter of 2021, the paper uses a nonlinear autoregressive distributed lag model to verify the asymmetric effects. Then the paper constructs a time-varying parameter vector autoregressive model with stochastic volatility term to analyze the nonlinear responses. Impulse response analysis was used to further explain the relationship between the three. The negative impact of rising EPU on urban residents’ consumption is larger than its reduction. Negative shocks to natural resource development increase the urban residents’ consumption. Positive shocks reduce the urban residents’ consumption. There is a time-varying non-linear effect of EPU and natural resource development on urban residents’ consumption in China. The negative impact of EPU on urban consumption has been further exacerbated by major crises such as the financial crisis, COVID-19 and the post-crisis period. The negative impact of natural resource development diminished after the government introduced industrial upgrading policies and environmental regulations. This study provides constructive suggestions for the optimization of economic policies and the improvement of urban consumption. This study also enriches consumer theory and provides new evidence for the resource curse hypothesis.