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2021 ◽  
Vol 241 ◽  
pp. 109981
Author(s):  
Yexuan Ma ◽  
Wanhai Xu ◽  
Huanan Ai ◽  
Yingying Wang ◽  
Kun Jia

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Sun Yapeng ◽  
Peng Hui ◽  
Xie Wenbiao

The non-linear market microstructure (MM) model for financial time series modeling is a flexible stochastic volatility model with demand surplus and market liquidity. The estimation of the model is difficult, since the unobservable surplus demand is a time-varying stochastic variable in the return equation, and the market liquidity arises both in the mean term and in the variance term of the return equation in the MM model. A fast and efficient Markov Chain Monte Carlo (MCMC) approach based on an efficient simulation smoother algorithm and an acceptance-rejection Metropolis–Hastings algorithm is designed to estimate the non-linear MM model. Since the simulation smoother algorithm makes use of the band diagonal structure and positive definition of Hessian matrix of the logarithmic density, it can quickly draw the market liquidity. In addition, we discuss the MM model with Student-t heavy tail distribution that can be utilized to address the presence of outliers in typical financial time series. Using the presented modeling method to make analysis of daily income of the S&P 500 index through the point forecast and the density forecast, we find clear support for time-varying volatility, volatility feedback effect, market microstructure theory, and Student-t heavy tails in the financial time series. Through this method, one can use the estimated market liquidity and surplus demand which is much smoother than the strong stochastic return process to assist the transaction decision making in the financial market.


2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sean Jewell ◽  
Joseph Futoma ◽  
Lauren Hannah ◽  
Andrew C. Miller ◽  
Nicholas J. Foti ◽  
...  

AbstractRestricting in-person interactions is an important technique for limiting the spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Although early research found strong associations between cell phone mobility and infection spread during the initial outbreaks in the United States, it is unclear whether this relationship persists across locations and time. We propose an interpretable statistical model to identify spatiotemporal variation in the association between mobility and infection rates. Using 1 year of US county-level data, we found that sharp drops in mobility often coincided with declining infection rates in the most populous counties in spring 2020. However, the association varied considerably in other locations and across time. Our findings are sensitive to model flexibility, as more restrictive models average over local effects and mask much of the spatiotemporal variation. We conclude that mobility does not appear to be a reliable leading indicator of infection rates, which may have important policy implications.


Author(s):  
Likun Wang ◽  
Narges Shahroudi ◽  
Eric Maddy ◽  
Kevin Garrett ◽  
Sid Boukabara ◽  
...  

AbstractDeveloped at the National Oceanic and Atmospheric Administration (NOAA) and the Joint Center for Satellite Data Assimilation (JCSDA), the Community Global Observing System Simulation Experiment (OSSE) Package (CGOP) provides a vehicle to quantitatively evaluate the impacts of emerging environmental observing systems or emerging in-situ or remote sensing instruments on NOAA numerical weather prediction (NWP) forecast skill. The typical first step for the OSSE is to simulate observations from the so-called “nature run”. Therefore, the observation spatial, temporal, and view geometry are needed to extract the atmospheric and surface variables from the nature run, which are then input to the observation forward operator (e.g., radiative transfer models) to simulate the new observations. This is a challenge for newly proposed systems for which instruments are not yet built or platforms are not yet deployed. To address this need, this study introduces an orbit simulator to compute these parameters based on the specific hosting platform and onboard instrument characteristics, which has been recently developed by the NOAA Center for Satellite Applications and Research (STAR) and added to the GCOP framework. In addition to simulating existing polar-orbiting and geostationary orbits, it is also applicable to emerging near space platforms (e.g., stratospheric balloons), cube satellite constellations, and Tundra orbits. The observation geometry simulator includes not only passive microwave and infrared sounders but also Global Navigation Satellite System/Radio Occultation (GNSS/RO) instruments. For passive atmospheric sounders, it calculates the geometric parameters of proposed instruments on different platforms, such as time varying location (latitude and longitude), scan geometry (satellite zenith and azimuth angles), and Ground Instantaneous Field of View (GIFOV) parameters for either cross-track or conical scanning mechanisms. For RO observations, it determines the geometry of the transmitters and receivers either on satellites or stratospheric balloons and computes their slant paths. The simulator has been successfully applied for recent OSSE studies (e.g., evaluating the impacts of future geostationary hyperspectral infrared sounders and RO observations from stratospheric balloons).


2021 ◽  
Author(s):  
Liang-Liang Yang ◽  
Xiang Luo ◽  
Rui Yuan ◽  
Hui Zhang

Abstract Traditional Optimal Iterative Learning Control (TOILC) can effectively improve the tracking performance of the servo system. However, there may be parameter perturbation in the running process of the servo system, and its parameters are constantly changing slowly. As a result, the convergence of TOILC becomes worse, and the tracking performance of the system deteriorates seriously. Therefore, in view of the time-varying characteristics of the system, a least squares optimal iterative learning control (LSAOILC) algorithm is proposed. In the process of iteration, the nominal model of the system is identified according to the input and output signals so as to update the optimal iterative learning controller, which does not need to obtain the exact system model information in advance, making up for the shortage of TOILC. The simulations and experiments prove the effectiveness of the proposed strategy for the servo system.


2021 ◽  
Vol 23 (1) ◽  
Author(s):  
C. A. Lechtenboehmer ◽  
T. Burkard ◽  
S. Reichenbach ◽  
U. A. Walker ◽  
A. M. Burden ◽  
...  

Abstract Objectives A considerable proportion of patients with rheumatoid arthritis (RA) also suffer from hand osteoarthritis (OA). We here assess the association between conventional synthetic (cs) and biological (b) disease-modifying antirheumatic drugs (DMARDs) and radiographic distal interphalangeal-(DIP) OA in patients with RA. Methods Adult RA patients from a longitudinal Swiss registry of rheumatic diseases who had ≥ 2 hand radiographs were included at the first radiograph and followed until the outcome or the last radiograph. Patients were grouped into two cohorts based on whether DIP OA was present or absent at cohort entry (cohorts 1 and 2, respectively). Modified Kellgren-Lawrence scores (KLS) were obtained by evaluating DIP joints for the severity of osteophytes, joint space narrowing, subchondral sclerosis, and erosions. KLS ≥ 2 in ≥ 1 DIP joint indicated incident or existing OA, and increase of ≥ 1 in KLS in ≥ 1 DIP joint indicated progression in existing DIP OA. Time-varying Cox regression and generalized estimating equation (GEE) analyses were performed. We estimated hazard ratios (HRs) and odds ratios (ORs) with 95% confidence intervals (CI) of DIP OA incidence (cohort 2), or progression (cohort 1), in bDMARD monotherapy, bDMARD/csDMARD combination therapy, and past or never DMARD use, when compared to csDMARD use. In post hoc analyses, we descriptively and analytically assessed the individual KLS features in cohort 1. Results Among 2234 RA patients with 5928 radiographs, 1340 patients had DIP OA at baseline (cohort 1). Radiographic progression of DIP OA was characterized by new or progressive osteophyte formation (666, 52.4%), joint space narrowing (379, 27.5%), subchondral sclerosis (238, 17.8%), or erosions (62, 4.3%). bDMARD monotherapy had an increased risk of radiographic DIP OA progression compared to csDMARD monotherapy (adjusted HR 1.34 [95% CI 1.07–1.69]). The risk was not significant in csDMARD/bDMARD combination users (HR 1.12 [95% CI 0.96–1.31]), absent in past DMARD users (HR 0.96 [95% CI 0.66–1.41]), and significantly lower among never DMARD users (HR 0.54 [95% CI 0.33–0.90]). Osteophyte progression (HR 1.74 [95% CI 1.11–2.74]) was the most significantly increased OA feature with bDMARD use compared to csDMARD use. In 894 patients without initial DIP OA (cohort 2), the risk of incident OA did not differ between the treatment groups. The results from GEE analyses corroborated all findings. Conclusions These real-world RA cohort data indicate that monotherapy with bDMARDs is associated with increased radiographic progression of existing DIP OA, but not with incident DIP OA.


2021 ◽  
Author(s):  
Uffe Høgsbro Thygesen ◽  
Maksim Mazuryn

Abstract We consider the collective motion of animals in time-varying environments, using as a case diel vertical migration in the ocean. The animals are distributed in space such that each animal moves optimally, seeking regions which offer high growth rates and low mortalities, subject to costs on excessive movements as well as being in regions with high densities of conspecifics. The model applies to repeated scenarios such as diel or seasonal patterns, where the animals are aware of both current and future environmental conditions. We show that this problem can be viewed as a differential game of mean field type, and that the evolutionary stable solution, i.e. the Nash equilibrium, is characterized by partial differential equations, which govern the distributions and migration velocities of animals. These equations have similarities to equations that appear in the fluid dynamics, specifically the Euler equations for compressible inviscid fluids. If the environment is constant, the ideal free distribution emerges as an equilibrium. We illustrate the theory with a numerical example of vertical animal movements in the ocean, where animals are attracted to nutrient-rich surface waters while repulsed from light during daytime due to the presence of visual predators, aiming to reduce both proximity to conspecifics and swimming efforts. For this case, we show that optimal movements are diel vertical migrations in qualitative agreement with observations.


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