Threshold Estimation Research Articles

Soaring inflation in sub-Saharan Africa: A fiscal root?

The study investigates the effect of fiscal policy on the inflation rate in a panel of 44 sub-Saharan African (SSA) countries over the period 2003-2020 using a non-linear system generalized method of moments (system GMM) and the dynamic panel threshold estimation techniques. The results show that the recent increase in inflation rate has a fiscal nature and that monetary policy alone may not provide an effective response. Specifically, the results indicate that a positive shock to fiscal policy (captured by public debts) has a positive and statistically significant effect on inflation, while a negative shock to public debt has a statistically non-significant impact on the inflation rate. Also, money supply exerted a positive and insignificant impact on inflation, indicating that the current inflation rate in the region may not be induced by money supply. However, the joint effect of public debts and money supply shows that public debts aid the effect of money supply on the inflation rate, albeit, not in the proportion predicted by the quantity theory of money. Further, the results also found a public debt threshold point of 60.59% of GDP. This implies the current inflationary pressure may be rooted in fiscal policy and that further accumulation of public debts beyond the benchmark established in the study would worsen the inflationary pressure in SSA. Importantly, the study found that for fiscal policy to spur growth and reduce inflationary pressure in SSA, the inflation rate should be managed and brought within a single-digit framework of 4%. The research and policy implications are discussed.

A new multimodal paradigm for biomarkers longitudinal monitoring: a clinical application to women steroid profiles in urine and blood

BackgroundMost current state-of-the-art strategies to generate individual adaptive reference ranges are designed to monitor one clinical parameter at a time. An innovative methodology is proposed for the simultaneous longitudinal monitoring of multiple biomarkers. The estimation of individual thresholds is performed by applying a Bayesian modeling strategy to a multivariate score integrating several biomarkers (compound concentration and/or ratio). This multimodal monitoring was applied to data from a clinical study involving 14 female volunteers with normal menstrual cycles receiving testosterone via transdermal route, as to test its ability to detect testosterone administration. The study samples consisted of urine and blood collected during 4 weeks of a control phase and 4 weeks with a daily testosterone gel application. ResultsIntegrating multiple biomarkers improved the detection of testosterone gel administration with substantially higher sensitivity compared with the distinct follow-up of each biomarker, when applied to selected urine and serum steroid biomarkers, as well as the combination of both. Among the 175 known positive samples, 38% were identified by the multimodal approach using urine biomarkers, 79% using serum biomarkers and 83% by combining biomarkers from both biological matrices, whereas 10%, 67% and 64% were respectively detected using standard unimodal monitoring. Significance and noveltyThe detection of abnormal patterns can be improved using multimodal approaches. The combination of urine and serum biomarkers reduced the overall number of false-negatives, thus evidencing promising complementarity between urine and blood sampling for doping control, as highlighted in the case of the use of transdermal testosterone preparations. The generation in a multimodal setting of adaptive and personalized reference ranges opens up new opportunities in clinical and anti-doping profiling. The integration of multiple parameters in a longitudinal monitoring is expected to provide a more complete evaluation of individual profiles generating actionable intelligence to further guide sample collection, analysis protocols and decision-making in clinics and anti-doping.

Multiresolution Dual-Polynomial Decomposition Approach for Optimized Characterization of Motor Intent in Myoelectric Control Systems.

Surface electromyogram (sEMG) is arguably the most sought-after physiological signal with a broad spectrum of biomedical applications, especially in miniaturized rehabilitation robots such as multifunctional prostheses. The widespread use of sEMG to drive pattern recognition (PR)-based control schemes is primarily due to its rich motor information content and non-invasiveness. Moreover, sEMG recordings exhibit non-linear and non-uniformity properties with inevitable interferences that distort intrinsic characteristics of the signal, precluding existing signal processing methods from yielding requisite motor control information. Therefore, we propose a multiresolution decomposition driven by dual-polynomial interpolation (MRDPI) technique for adequate denoising and reconstruction of multi-class EMG signals to guarantee the dual-advantage of enhanced signal quality and motor information preservation. Parameters for optimal MRDPI configuration were constructed across combinations of thresholding estimation schemes and signal resolution levels using EMG datasets of amputees who performed up to 22 predefined upper-limb motions acquired in-house and from the public NinaPro database. Experimental results showed that the proposed method yielded signals that led to consistent and significantly better decoding performance for all metrics compared to existing methods across features, classifiers, and datasets, offering a potential solution for practical deployment of intuitive EMG-PR-based control schemes for multifunctional prostheses and other miniaturized rehabilitation robotic systems that utilize myoelectric signals as control inputs.

Psychometric Evaluation of the Activity Impairment in Migraine-Diary (AIM-D) Using Data From a Phase 3 (PROGRESS) Clinical Trial in Patients With Chronic Migraine (P8-12.008)

Psychometric Evaluation of the Activity Impairment in Migraine-Diary (AIM-D) Using Data From a Phase 3 (PROGRESS) Clinical Trial in Patients With Chronic Migraine (P8-12.008)

Bias reduction estimation for drift coefficient in diffusion models with jumps

In this paper, we reconstruct the local linear threshold estimator for the drift coefficient of a semimartingale with jumps. Under mild conditions, we provide the asymptotic normality of our estimator in the presence of finite activity jumps whether the underlying process is Harris recurrent or positive recurrent. Simulation studies for different models show that our estimator performs better than previous research in finite samples, which can correct the boundary bias automatically. Finally, the estimator is illustrated empirically through the stock index from Shanghai Stock Exchange in China under 15-minute high sampling frequency.

A Model-Based Estimate of the Cost-Effectiveness Threshold in Germany.

Value-based pricing of new, innovative health technologies defined as pricing through economic evaluation requires the use of a basic cost-effectiveness threshold. This study presents a cost-effectiveness model that determines the cost-effectiveness threshold for life-extending new, innovative technologies based on health system opportunity costs. To estimate health system opportunity costs, the study used German data and examined the period between 1896 and 2014. To this end, it determined intertemporal differences in the remaining lifetime spending and life expectancy by age and gender. To account for the age composition of the population, it weighted age-specific intertemporal changes in the remaining lifetime spending and life expectancy by the age-specific population size. To estimate life expectancy gains solely attributable to the health care system, it used aggregated data on amenable mortality. It calculated the cost-effectiveness ratio of health care spending in the German health care system on average and at the margin. Based on the cost-effectiveness ratio of health care spending at the margin, the threshold value for life-prolonging new, innovative technologies was at least €42,634 per life-year gained, with a point estimate of €88,107 per life-year gained. Based on the average ratio, the threshold value dropped below €34,000 per life-year gained. This study provides new evidence on the cost-effectiveness threshold for value-based pricing of new, innovative technologies. Data from Germany suggest that a threshold value based on health care spending at the margin is considerably higher than that based on the average ratio.

Positive-definite thresholding estimators of covariance matrices with zeros

A positive definite estimator of a covariance matrix with zero entries provides a valid covariance matrix that can be used an input in almost any area of multivariate statistical analysis. However, most current approaches do not yet guarantee positive definiteness or deal with the asymptotic efficiency of the covariance estimator. Focusing on the classical setting when the number of Gaussian variables is fixed and the sample size increases, we construct a positive definite and asymptotically efficient estimator by the iterative conditional fitting algorithm (Chaudhuri et al., 2007) when the location of the zero entries is known. If the location of the zero entries is unknown, we further construct a positive definite thresholding estimator by combining the iterative conditional fitting algorithm with thresholding. We prove our thresholding estimator is asymptotically efficient with probability tending to one. In simulation studies, we show our estimator more closely matches the true covariance and more correctly identifies the non-zero entries than competing estimators. We apply our estimator to a neuroimaging study of Huntington disease to detect non-zero correlations among brain regional volumes. Such correlations are timely for ongoing treatment studies to inform how different brain regions are likely to be affected by these treatments.

Effects of credit and labor constraints on microenterprises and the unintended impact of changes in household endowments: Use of threshold estimation to detect heterogeneity

Effects of credit and labor constraints on microenterprises and the unintended impact of changes in household endowments: Use of threshold estimation to detect heterogeneity

Investigations of nonlinear polarization transfer between obliquely intersecting beams

Electromagnetic waves propagating through plasma can interact nonlinearly through a variety of different mechanisms. The excitation of a plasma beat wave (ions or electrons) can create a refractive index modulation that changes the dispersion of the interacting beams. Alternatively, high-intensity beams can enter the regime where relativistic nonlinearities influence the propagation dynamics. In recent studies [Opt. Express 29, 1162 (2021)OPEXFF1094-408710.1364/OE.413064], it was proposed that two beams propagating along the same axis can exchange their polarization state due to nonlinear interaction. Here we present a numerical analysis of two laser beams intersecting in a nonlinear medium at varying angles. Polarization transfer is observed as predicted by analytical theory for a range of angles. For small angles, it is found that filamentation of the interacting beams becomes important. Analytical estimates of the filamentation threshold are presented, and good agreement is found with the simulation data.

Rainfall thresholds estimation for shallow landslides in Peru from gridded daily data

Abstract. This work aims to generate and evaluate regional rainfall thresholds obtained from a combination of high-resolution gridded rainfall data, developed by the National Service of Meteorology and Hydrology of Peru, and information from observed shallow landslide events. The landslide data were associated with rainfall data, determining triggering and non-triggering rainfall events with rainfall properties from which rainfall thresholds are determined. The validation of the performance of the thresholds is carried out with events that occurred during 2020 and focuses on evaluating the operability of these thresholds in landslide warning systems in Peru. The thresholds are determined for 11 rainfall regions. The method of determining the thresholds is based on an empirical–statistical approach, and the predictive performance of the thresholds is evaluated with true skill statistics. The best predictive performance is the mean daily intensity–duration (Imean−D) threshold curve, followed by accumulated rainfall E. This work is the first estimation of regional thresholds on a country scale to better understand landslides in Peru, and the results obtained reveal the potential of using thresholds in the monitoring and forecasting of shallow landslides caused by intense rainfall and in supporting the actions of disaster risk management.

An Evaluation of the Optimal Inflation Target for Economic Growth in Nigeria

This study uses the threshold regression method to determine the optimal inflation threshold for economic growth in Nigeria. There is evidence that establish that setting an inflation target as a nominal anchor that constrains price movements to a particular point or within a pre-agreed range is indispensable for economic growth. Application of the Bai-Perron structural breakpoints test to the data detected two breaks dated 1999 and 2011. The Lee-Strazicich Unit Root Test showed that the data were cointegrated. The threshold estimation for optimal inflation values in the 5% to 25% band revealed that the residual sums of squares, RSS, is minimized and the R2 is maximized at an optimal inflation threshold of 18% with a statistically significant regression model, suggesting that the optimal inflation target for economic growth in Nigeria is 18%. The Autoregressive Distributed Lag model (ARDL) estimate shows that a 1% increase in inflation leads to an increase of 0.017% GDP growth in Nigeria in the long-run. The estimation of the ARDL model indicate that the coefficients of exchange rate, trade openness and population growth present mixed signaling in relation to GDP growth in the long- and short-run. Our results indicate that the implementation of the inflation targeting framework in a credible, transparent and accountable way could help to anchor inflation expectations, rein in inflation and improve economic growth in Nigeria.

Unsupervised SAR Image Change Detection Based on Structural Consistency and CFAR Threshold Estimation

Despite the remarkable progress made in recent years, until today, the automatic detection of changes in synthetic aperture radar (SAR) images remains a difficult task due to speckle noise. This inherent multiplicative noise tends to increase false alarms and misdetections. As a solution, we developed an unsupervised method that detects SAR changes by analyzing structural differences. By this method, the spatial structure cues of a pixel are represented by a set of similarity weight vectors calculated from the non-local scale of the pixel. The difference image (DI) is then derived by measuring the structural consistency of the corresponding pixels. A new statistical distance that is insensitive to speckle noise was used to measure the similarity weights between patches in order to obtain an accurate structure. It was derived by applying the Nakagami–Rayleigh distribution to a statistical test and customizing the approximation based on change detection. The CFAR threshold estimator in conjunction with the Rayleigh hypothesis was then employed to attenuate the effect of the unimodal histogram of the DI. The results indicated that the proposed method reduces the false alarm rate and improves the kappa and F1-scores, while providing satisfactory visual results.

An adaptive Bayesian algorithm for efficient auditory brainstem response threshold estimation: Numerical validation

The auditory brainstem response (ABR) can be used to estimate the hearing threshold of animals or human subjects who are unable to respond to behavioral measures. However, ABR can be time-intensive and is vulnerable to human subjectivity while interpreting the waveforms. An adaptive procedure has been developed to efficiently and objectively estimate ABR threshold. The procedure iteratively fits a Gaussian process (GP) model to the ABR waveforms collected so far and optimizes the subsequent stimulus. The algorithm was validated through numerical simulation using pre-collectedhuman ABR at 0.5, 1.0, 2.0, and 4.0 kHz with levels from below threshold to 90 dB in 5 dB increments from a cohort of normal-hearing listeners. This led to a full stimulus space of approximately 55 stimuli per test ear. For each test ear, the ABR threshold was estimated by human raters based on the entire stimulus space. The ABR threshold was also estimated using the adaptive procedure, which iteratively sampled a subset (∼35%) of the stimulus space. At the end of the adaptive procedure, the fitted GP model was able to capture the individual differences in waveform morphology between listeners. Furthermore, the threshold estimates from repeated runs of the adaptive procedure demonstrated adequate test-retest reliability.

International financial integration: Too much?

According to theory, financial openness (FO) increases growth. The literature often conditions the growth effect of FO on favorable collateral environment. However, this can conceal the actual growth benefits of FO. This paper contributes to the literature by investigating the unconditional growth effect of FO, measured as de facto international financial integration (IFI). We maintain that the level of IFI might affect the structure of this relationship. We examine this important issue in advanced and emerging market and developing economies over the 990-209 period using conditional and unconditional growth regressions. Panel fixed effects threshold and dynamic panel threshold estimations suggest that the IFI-growth relation is conditional on data-driven estimated threshold level of IFI. Accordingly, IFI encourages (impedes) growth in less (more) financially integrated economies. The results show that it is not impossible to finance growth with IFI, but it might be risky, especially beyond a certain threshold level of IFI.

Shrinkage estimation of multiple threshold factor models

This paper proposes a multiple threshold factor model to enhance the flexibility in modeling the underlying regime switching mechanism for high dimensional time series. The factor loadings are assumed to switch between different regimes according to the value of a threshold variable. A novel estimation procedure is proposed to consistently estimate the multiple thresholds with the aid of sorting operation, principal component analysis and shrinkage estimation, which is practically easy-to-implement and computationally efficient. Furthermore, asymptotic properties for the multiple threshold estimators are established, together with other theoretical results. Monte Carlo simulations demonstrate that the procedure works well in finite samples. The U.S. data sets are analyzed with the proposed model to illustrate the threshold effect of economy policy uncertainty on the financial market.

Threshold estimation for jump-diffusions under small noise asymptotics

We consider parameter estimation of stochastic differential equations driven by a Wiener process and a compound Poisson process as small noises. The goal is to give a threshold-type quasi-likelihood estimator and show its consistency and asymptotic normality under new asymptotics. One of the novelties of the paper is that we give a new localization argument, which enables us to avoid truncation in the contrast function that has been used in earlier works and to deal with a wider class of jumps in threshold estimation than ever before.

Structural damage detection of the bridge under moving loads with the quasi-static displacement influence line from one sensor

As the bridges continue to age, it is imperative to identify structurally deficient bridges at an early stage to prevent unexpected maintenance needs. A quasi-static method is developed to identify structural damage based on the influence line (IL) from one sensor and an empirical Bayesian threshold estimator. Initially, the empirical Bayesian threshold estimator is utilized to preprocess the measured bridge displacement response to eliminate vehicle-induced dynamic effects. Subsequently, a regularization technique using Elastic Net and displacement response is used for IL estimation, and the Elastic Net that integrates with both L1 and L2 norms is adopted to establish the objective function of IL estimation to deal with the ill-posedness of the inverse problem for specific vehicle configurations. Finally, according to the mechanical relationship between the moving load and the displacement IL, an IL area-based difference index is established to identify the presence of the damage rapidly, raising a flag that leads to a more in-depth investigation to gather information related to structural integrity. The feasibility of the proposed method is verified with the numerical model and laboratory tests. This study introduces innovative contributions in the following three aspects: (1) Elastic Net that integrates with both L1 and L2 norms is adopted to establish the objective function for IL estimation, (2) A single sensor provides a cost-effective and less computationally expensive solution than multi-sensor methods, (3) Compared to other wavelet-based methods, the comparative studies demonstrate that empirical Bayesian threshold estimator can identify damage more clearly.

Droplet finite-size scaling of the contact process on scale-free networks revisited

We present an alternative finite-size scaling (FSS) of the contact process on scale-free networks compatible with mean-field scaling and test it with extensive Monte Carlo simulations. In our FSS theory, the dependence on the system size enters the external field, which represents spontaneous contamination in the context of an epidemic model. In addition, dependence on the finite size in the scale-free networks also enters the network cutoff. We show that our theory reproduces the results of other mean-field theories on finite lattices already reported in the literature. To simulate the dynamics, we impose quasi-stationary states by reactivation. We insert spontaneously infected individuals, equivalent to a droplet perturbation to the system scaling as [Formula: see text]. The system presents an absorbing phase transition where the critical behavior obeys the mean-field exponents, as we show theoretically and by simulations. However, the quasi-stationary state gives finite-size logarithmic corrections, predicted by our FSS theory, and reproduces equivalent results in the literature in the thermodynamic limit. We also report the critical threshold estimates of basic reproduction number [Formula: see text] of the model as a linear function of the network connectivity inverse [Formula: see text], and the extrapolation of the critical threshold function for [Formula: see text] yields the basic reproduction number [Formula: see text] of the complete graph, as expected. Decreasing the network connectivity increases the critical [Formula: see text] for this model.

Estimating meaningful thresholds for multi-item questionnaires using item response theory

PurposeMeaningful thresholds are needed to interpret patient-reported outcome measure (PROM) results. This paper introduces a new method, based on item response theory (IRT), to estimate such thresholds. The performance of the method is examined in simulated datasets and two real datasets, and compared with other methods.MethodsThe IRT method involves fitting an IRT model to the PROM items and an anchor item indicating the criterion state of interest. The difficulty parameter of the anchor item represents the meaningful threshold on the latent trait. The latent threshold is then linked to the corresponding expected PROM score. We simulated 4500 item response datasets to a 10-item PROM, and an anchor item. The datasets varied with respect to the mean and standard deviation of the latent trait, and the reliability of the anchor item. The real datasets consisted of a depression scale with a clinical depression diagnosis as anchor variable and a pain scale with a patient acceptable symptom state (PASS) question as anchor variable.ResultsThe new IRT method recovered the true thresholds accurately across the simulated datasets. The other methods, except one, produced biased threshold estimates if the state prevalence was smaller or greater than 0.5. The adjusted predictive modeling method matched the new IRT method (also in the real datasets) but showed some residual bias if the prevalence was smaller than 0.3 or greater than 0.7.ConclusionsThe new IRT method perfectly recovers meaningful (interpretational) thresholds for multi-item questionnaires, provided that the data satisfy the assumptions for IRT analysis.

Improved Estimation of Exercise Intensity Thresholds by Combining Dual Non-Invasive Biomarker Concepts: Correlation Properties of Heart Rate Variability and Respiratory Frequency.

Identifying exercise intensity boundaries has been shown to be important during endurance training for performance enhancement and rehabilitation. Unfortunately, even though surrogate markers show promise when assessed on a group level, substantial deviation from gold standards can be present in each individual. The aim of this study was to evaluate whether combining two surrogate intensity markers improved this agreement. Electrocardiogram (ECG) and gas exchange data were obtained from 21 participants who performed an incremental cycling ramp to exhaustion and evaluated for first (VT1) and second (VT2) ventilatory thresholds, heart rate (HR) variability (HRV), and ECG derived respiratory frequency (EDR). HRV thresholds (HRVT) were based on the non-linear index a1 of a Detrended Fluctuation Analysis (DFA a1) and EDR thresholds (EDRT) upon the second derivative of the sixth-order polynomial of EDR over time. The average of HRVT and EDRT HR was set as the combined threshold (Combo). Mean VT1 was reached at a HR of 141 ± 15, HRVT1 at 152 ± 14 (p < 0.001), EDRT1 at 133 ± 12 (p < 0.001), and Combo1 at 140 ± 13 (p = 0.36) bpm with Pearson's r of 0.83, 0.78, and 0.84, respectively, for comparisons to VT1. A Bland-Altman analysis showed mean biases of 8.3 ± 7.9, -8.3 ± 9.5, and -1.7 ± 8.3 bpm, respectively. A mean VT2 was reached at a HR of 165 ± 13, HRVT2 at 167 ± 10 (p = 0.89), EDRT2 at 164 ± 14 (p = 0.36), and Combo2 at 164 ± 13 (p = 0.59) bpm with Pearson's r of 0.58, 0.95, and 0.94, respectively, for comparisons to VT2. A Bland-Altman analysis showed mean biases of -0.3 ± 8.9, -1.0 ± 4.6, and -0.6 ± 4.6 bpm, respectively. Both the DFA a1 and EDR intensity thresholds based on HR taken individually had moderate agreement to targets derived through gas exchange measurements. By combining both non-invasive approaches, there was improved correlation, reduced bias, and limits of agreement to the respective corresponding HRs at VT1 and VT2.