We consider the problem of estimating time-localized cross-dependence in a collection of nonstationary signals. To this end, we develop the multivariate locally stationary wavelet framework, which ...provides a time-scale decomposition of the signals and, thus, naturally captures the time-evolving scale-specific cross-dependence between components of the signals. Under the proposed model, we rigorously define and estimate two forms of cross-dependence measures: wavelet coherence and wavelet partial coherence. These dependence measures differ in a subtle but important way. The former is a broad measure of dependence, which may include indirect associations, i.e., dependence between a pair of signals that is driven by another signal. Conversely, wavelet partial coherence measures direct linear association between a pair of signals, i.e., it removes the linear effect of other observed signals. Our time-scale wavelet partial coherence estimation scheme thus provides a mechanism for identifying hidden dynamic relationships within a network of nonstationary signals, as we demonstrate on electroencephalograms recorded in a visual-motor experiment.
In this paper we consider Bayesian parameter inference for partially observed fractional Brownian motion models. The approach we follow is to time-discretize the hidden process and then to design ...Markov chain Monte Carlo (MCMC) algorithms to sample from the posterior density on the parameters given data. We rely on a novel representation of the time discretization, which seeks to sample from an approximation of the posterior and then corrects via importance sampling; the approximation reduces the time (in terms of total observation time
T
) by
O
(
T
)
. This method is extended by using a multilevel MCMC method which can reduce the computational cost to achieve a given mean square error versus using a single time discretization. Our methods are illustrated on simulated and real data.
The sex ratio at birth (SRB; ratio of male to female births) in Nepal has been reported around the normal level on the national level. However, the national SRB could mask the disparity within the ...country. Given the demographic and cultural heterogeneities in Nepal, it is crucial to model Nepal SRB on the subnational level. Prior studies on subnational SRB in Nepal are mostly based on reporting observed values from surveys and census, and no study has provided probabilistic projections. We aim to estimate and project SRB for the seven provinces of Nepal from 1980 to 2050 using a Bayesian modeling approach.
We compiled an extensive database on provincial SRB of Nepal, consisting 2001, 2006, 2011, and 2016 Nepal Demographic and Health Surveys and 2011 Census. We adopted a Bayesian hierarchical time series model to estimate and project the provincial SRB, with a focus on modelling the potential SRB imbalance.
In 2016, the highest SRB is estimated in Province 5 (Lumbini Pradesh) at 1.102, corresponding to 110.2 male births per 100 female births, with a 95% credible interval (1.044, 1.127) and the lowest SRB is in Province 2 at 1.053 (1.035, 1.109). The SRB imbalance probabilities in all provinces are generally low and vary from 16% in Province 2 to 81% in Province 5 (Lumbini Pradesh). SRB imbalances are estimated to have begun at the earliest in 2001 in Province 5 (Lumbini Pradesh) with a 95% credible interval (1992, 2022) and the latest in 2017 (1998, 2040) in Province 2. We project SRB in all provinces to begin converging back to the national baseline in the mid-2030s. By 2050, the SRBs in all provinces are projected to be around the SRB baseline level.
Our findings imply that the majority of provinces in Nepal have low risks of SRB imbalance for the period 1980-2016. However, we identify a few provinces with higher probabilities of having SRB inflation. The projected SRB is an important illustration of potential future prenatal sex discrimination and shows the need to monitor SRB in provinces with higher possibilities of SRB imbalance.
Cardiac signals have complex structures representing a combination of simpler structures. In this paper, we develop a new data analytic tool that can extract the complex structures of cardiac signals ...using the framework of multi-chaotic analysis, which is based on the
-norm for calculating the largest Lyapunov exponent (
). Appling the
-norm is useful for deriving the spectrum of the generalized largest Lyapunov exponents (
, which is characterized by the width of the spectrum (which we denote by
). This quantity measures the degree of multi-chaos of the process and can potentially be used to discriminate between different classes of cardiac signals. We propose the joint use of the
and spectrum width to investigate the multi-chaotic behavior of inter-beat (R-R) intervals of cardiac signals recorded from 54 healthy subjects (hs), 44 subjects diagnosed with congestive heart failure (chf), and 25 subjects diagnosed with atrial fibrillation (af). With the proposed approach, we build a regression model for the diagnosis of pathology. Multi-chaotic analysis showed a good performance, allowing the underlying dynamics of the system that generates the heart beat to be examined and expert systems to be built for the diagnosis of cardiac pathologies.
The sex ratio at birth (SRB, i.e., the ratio of male to female births) in Vietnam has been imbalanced since the 2000s. Previous studies have revealed a rapid increase in the SRB over the past 15 ...years and the presence of important variations across regions. More recent studies suggested that the nation’s SRB may have plateaued during the 2010s. Given the lack of exhaustive birth registration data in Vietnam, it is necessary to estimate and project levels and trends in the regional SRBs in Vietnam based on a reproducible statistical approach. We compiled an extensive database on regional Vietnam SRBs based on all publicly available surveys and censuses and used a Bayesian hierarchical time series mixture model to estimate and project SRB in Vietnam by region from 1980 to 2050. The Bayesian model incorporates the uncertainties from the observations and year-by-year natural fluctuation. It includes a binary parameter to detect the existence of sex ratio transitions among Vietnamese regions. Furthermore, we model the SRB imbalance using a trapezoid function to capture the increase, stagnation, and decrease of the sex ratio transition by Vietnamese regions. The model results show that four out of six Vietnamese regions, namely, Northern Midlands and Mountain Areas, Northern Central and Central Coastal Areas, Red River Delta, and South East, have existing sex imbalances at birth. The rise in SRB in the Red River Delta was the fastest, as it took only 12 years and was more pronounced, with the SRB reaching the local maximum of 1.146 with a 95% credible interval (1.129, 1.163) in 2013. The model projections suggest that the current decade will record a sustained decline in sex imbalances at birth, and the SRB should be back to the national SRB baseline level of 1.06 in all regions by the mid-2030s.
The sex ratio at birth (SRB) in India has been reported to be imbalanced since the 1970s. Previous studies have shown there is a great variation in the SRB between geographic locations across India ...till 2016. Considering the enormous population and regional heterogeneity of India, producing probabilistic SRB projections at the state level is crucial for policy planning and population projection. In this paper, we implement a Bayesian hierarchical time series model to project the SRB across India by state. We generate SRB probabilistic projections from 2017 to 2030 for 29 States and Union Territories (UTs) in India, and present results for 21 States/UTs with data available from the Sample Registration System. Our analysis takes into account two state-specific factors that contribute to sex-selective abortion in India, resulting in sex imbalances at birth: the intensity of son preference and fertility squeeze. We project that the highest deficits in female births will occur in Uttar Pradesh, with a cumulative number of missing female births of 2.0 (95% credible interval 1.9; 2.2) million from 2017 to 2030. The total female birth deficits during 2017-2030 for the whole of India is projected to be 6.8 6.6; 7.0 million.
Objective: High-frequency oscillations (HFOs) are a promising prognostic biomarker of surgical outcome in patients with epilepsy. Their rates of occurrence and morphology have been studied ...extensively using recordings from electrodes of various geometries. While electrode size is a potential confounding factor in HFO studies, it has largely been disregarded due to a lack of consistent evidence. Therefore, we designed an experiment to directly test the impact of electrode size on HFO measurement. Methods: We first simulated HFO measurement using a lumped model of the electrode-tissue interaction. Then eight human subjects were each implanted with a high-density 8x8 grid of subdural electrodes. After implantation, the electrode sizes were altered using a technique recently developed by our group, enabling intracranial EEG recordings for three different electrode surface areas from a static brain location. HFOs were automatically detected in the data and their characteristics were calculated. Results: The human subject measurements were consistent with the model. Specifically, HFO rate measured per area of tissue decreased significantly as electrode surface area increased. The smallest electrodes recorded more fast ripples than ripples. Amplitude of detected HFOs also decreased as electrode surface area increased, while duration and peak frequency were unaffected. Conclusion: These results suggest that HFO rates measured using electrodes of different surface areas cannot be compared directly. Significance: This has significant implications for HFOs as a tool for surgical planning, particularly for individual patients implanted with electrodes of multiple sizes and comparisons of HFO rate made across patients and studies.
Recent reports of warming trends in the Red Sea raise concerns about the response of the basin's fragile ecosystem under an increasingly warming climate. Using a variety of available Sea Surface ...Temperature (SST) data sets, we investigate the evolution of Red Sea SST in relation to natural climate variability. Analysis of long‐term SST data sets reveals a sequence of alternating positive and negative trends, with similar amplitudes and a periodicity of nearly 70 years associated with the Atlantic Multidecadal Oscillation. High warming rates reported recently appear to be a combined effect of global warming and a positive phase of natural SST oscillations. Over the next decades, the SST trend in the Red Sea purely related to global warming is expected to be counteracted by the cooling Atlantic Multidecadal Oscillation phase. Regardless of the current positive trends, projections incorporating long‐term natural oscillations suggest a possible decreasing effect on SST in the near future.
Plain Language Summary
The recent warming trend of the Red Sea SST is critical for the fragile basin's ecosystem, especially for its precious coral reef community. Several studies based on satellite‐era data sets recently reported warming rates that can rapidly alter the environmental status of the Red Sea in the near future. We show that the long‐term variation of the SST over the Red Sea is influenced by a natural oscillation related to the Atlantic Multidecadal Oscillation. Satellite‐era data sets coincide with a positive phase of this oscillation and the associated SST trend is overstated compared to the real, long term trend. As Atlantic Multidecadal Oscillation currently shifts from positive to negative phase, the Red Sea SST is expected to shift into a cooling phase during the next decades.
Key Points
The Red Sea SST exhibits long‐term oscillations related to the Atlantic Multidecadal Oscillation
High warming trends for the period 1980 to 2010 are dominated by a positive phase of the natural SST oscillation
SST over the Red Sea is expected to shift to a cooling phase during the next few decades
Various interacting and interdependent components comprise complex interventions. These components create difficulty in assessing the true impact of interventions designed to improve patient-centered ...outcomes. Interrupted time series (ITS) designs borrow from case-crossover designs and serve as quasi-experimental methodology able to retrospectively assess the impact of an intervention while accounting for temporal correlation. While ITS designs are aptly situated for studying the impacts of large-scale public health policies, existing ITS software implement rigid ITS methodology that often assume the pre- and post-intervention phases are fully differentiated (by a known change-point or set of time points) and do not allow for changes in both the mean functions and correlation structure.
This article describes the Robust Interrupted Time Series (RITS) toolbox, a stand-alone user-friendly application researchers can use to implement flexible ITS models that estimate the lagged effect of an intervention on an outcome, level and trend changes, and post-intervention changes in the correlation structure, for single and multiple ITS. The RITS toolbox incorporates a formal test for the existence of a change in the outcome and estimates a change-point over a set of possible change-points defined by the researcher. In settings with multiple ITS, RITS provides a global over-all units change-point and allows for unit-specific changes in the mean functions and correlation structures.
The RITS toolbox is the first piece of software that allows researchers to use flexible ITS models that test for the existence of a change-point, estimate the change-point (if estimation is desired), and allow for changes in both the mean functions and correlation structures at the change point. RITS does not require any knowledge of a statistical (or otherwise) programming language, is freely available to the community, and may be downloaded and used on a local machine to ensure data protection.
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