Consistent Estimates Research Articles

Regional price differences of medical services: evidence from China

BackgroundPrice levels of medical services may vary across regions with different income levels, which would raise concerns about the equal access to medical services. This study aimed to estimate the spatial price index of medical services to measure price levels across 31 provincial regions in China.MethodsPrice data were collected from medical service price schedule in each region. Two methods based on the Purchasing Power Parities were used to estimate the spatial price index and measure price differences across regions. The two-way fixed effects models were used to examine the association between medical service price levels and income levels, and further investigate the impacts of price differences on utilization of medical services and medical expenditure.ResultsThe consistent estimation results were given by two methods. Medical service price level in the highest-price region was found to be 74% higher than the lowest. There was a significant negative correlation between price levels and income levels, as well as price levels and the utilization of outpatient services. Moreover, we also found a 1% increase in medical service price level was significantly associated with a 0.34% and 0.24% increase in the medical service expense per outpatient visit and per inpatient respectively.ConclusionsRegions in China had significant gaps in medical service price levels. Policymakers should pay more attention to regional price differences and take great measures such as enhancing financial protection to ensure the equal access to medical services and better achieve the universal health coverage.

Statistical Inference for Hüsler–Reiss Graphical Models Through Matrix Completions

The severity of multivariate extreme events is driven by the dependence between the largest marginal observations. The Hüsler–Reiss distribution is a versatile model for this extremal dependence, and it is usually parameterized by a variogram matrix. In order to represent conditional independence relations and obtain sparse parameterizations, we introduce the novel Hüsler–Reiss precision matrix. Similarly to the Gaussian case, this matrix appears naturally in density representations of the Hüsler–Reiss Pareto distribution and encodes the extremal graphical structure through its zero pattern. For a given, arbitrary graph we prove the existence and uniqueness of the completion of a partially specified Hüsler–Reiss variogram matrix so that its precision matrix has zeros on non-edges in the graph. Using suitable estimators for the parameters on the edges, our theory provides the first consistent estimator of graph structured Hüsler–Reiss distributions. If the graph is unknown, our method can be combined with recent structure learning algorithms to jointly infer the graph and the corresponding parameter matrix. Based on our methodology, we propose new tools for statistical inference of sparse Hüsler–Reiss models and illustrate them on large flight delay data in the United States, as well as Danube river flow data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

A consistent budgeting of terrestrial carbon fluxes

Accurate estimates of CO2 emissions from anthropogenic land-use change (ELUC) and of the natural terrestrial CO2 sink (SLAND) are crucial to precisely know how much CO2 can still be emitted to meet the goals of the Paris Agreement. In current carbon budgets, ELUC and SLAND stem from two model families that differ in how CO2 fluxes are attributed to environmental and land-use changes, making their estimates conceptually inconsistent. Here we provide consistent estimates of ELUC and SLAND by integrating environmental effects on land carbon into a spatially explicit bookkeeping model. We find that state-of-the-art process-based models overestimate SLAND by 23% (min: 8%, max: 33%) in 2012–2021, as they include hypothetical sinks that in reality are lost through historical ecosystem degradation. Additionally, ELUC increases by 14% (8%, 23%) in 2012–2021 when considering environmental effects. Altogether, we find a weaker net land sink, which makes reaching carbon neutrality even more ambitious. These results highlight that a consistent estimation of terrestrial carbon fluxes is essential to assess the progress of net-zero emission commitments and the remaining carbon budget.

Distance-based Clustering of Functional Data with Derivative Principal Component Analysis

Functional data analysis (FDA) is an important modern paradigm for handling infinite-dimensional data. An important task in FDA is clustering, which identifies subgroups based on the shapes of measured curves. Considering that derivatives can provide additional useful information about the shapes of functionals, we propose a novel L 2 distance between two random functions by incorporating the functions and their derivative information to determine the dissimilarity of curves under a unified scheme for dense observations. The Karhunen–Loève expansion is used to approximate the curves and their derivatives. Cluster membership prediction for each curve intends to minimize the new distances between the observed and predicted curves through subspace projection among all possible clusters. We provide consistent estimators for the curves, curve derivatives, and the proposed distance. Identifiability issues of the clustering procedure are also discussed. The utility of the proposed method is illustrated via simulation studies and applications to two real datasets. The proposed method can considerably improve cluster performance compared with existing functional clustering methods. Supplementary materials for the article are available online.

Boosting the HP filter for trending time series with long-range dependence

This article extends recent asymptotic theory developed for the Hodrick Prescott (HP) filter and boosted HP (bHP) filter to long-range dependent time series that have fractional Brownian motion (fBM) limit processes after suitable standardization. Under general conditions, it is shown that the asymptotic form of the HP filter is a smooth curve, analogous to the finding in Phillips and Jin for integrated time series and series with deterministic drifts. Boosting the filter using the iterative procedure suggested in Phillips and Shi leads under well-defined rate conditions to a consistent estimate of the fBM limit process or the fBM limit process with an accompanying deterministic drift when that is present. A stopping criterion is used to automate the boosting algorithm, giving a data-determined method for practical implementation. The theory is illustrated in simulations and two real data examples that highlight the differences between simple HP filtering and the use of boosting. The analysis is assisted by employing a uniformly and almost surely convergent trigonometric series representation of fBM.

Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models

Spatial auto-regressive (SAR) models are widely used in geosciences for data analysis; their main feature is the presence of weight (W) matrices, which define the neighboring relationships between the spatial units. The statistical properties of parameter and forecast estimates strongly depend on the structure of such matrices. The least squares (LS) method is the most flexible and can estimate systems of large dimensions; however, it is biased in the presence of multilateral (sparse) matrices. Instead, the unilateral specification of SAR models provides triangular weight matrices that allow consistent LS estimates and sequential prediction functions. These two properties are strictly related and depend on the linear and recursive nature of the system. In this paper, we show the better performance in out-of-sample forecasting of unilateral SAR (estimated with LS), compared to multilateral SAR (estimated with maximum likelihood, ML). This conclusion is supported by numerical simulations and applications to real geological data, both on regular lattices and irregularly distributed points.

An Integrated Navigation Method Aided by Position Correction Model and Velocity Model for AUVs.

When autonomous underwater vehicles (AUVs) perform underwater tasks, the absence of GPS position assistance can lead to a decrease in the accuracy of traditional navigation systems, such as the extended Kalman filter (EKF), due to the accumulation of errors. To enhance the navigation accuracy of AUVs in the absence of position assistance, this paper proposes an innovative navigation method that integrates a position correction model and a velocity model. Specifically, a velocity model is developed using a dynamic model and the Optimal Pruning Extreme Learning Machine (OP-ELM) method. This velocity model is trained online to provide velocity outputs during the intervals when the Doppler Velocity Log (DVL) is not updating, ensuring more consistent and reliable velocity estimation. Additionally, a position correction model (PCM) is constructed, based on a hybrid gated recurrent neural network (HGRNN). This model is specifically designed to correct the AUV's navigation position when GPS data are unavailable underwater. The HGRNN utilizes historical navigation data and patterns learned during training to predict and adjust the AUV's estimated position, thereby reducing the drift caused by the lack of real-time position updates. Experimental results demonstrate that the proposed VM-PCM-EKF algorithm can significantly improve the positioning accuracy of the navigation system, with a maximum accuracy improvement of 87.2% compared to conventional EKF algorithms. This method not only improves the reliability and accuracy of AUV missions but also opens up new possibilities for more complex and extended underwater operations.

Stone's theorem for distributional regression in Wasserstein distance

We extend the celebrated Stone's theorem to the framework of distributional regression. More precisely, we prove that weighted empirical distributions with local probability weights satisfying the conditions of Stone's theorem provide universally consistent estimates of the conditional distributions, where the error is measured by the Wasserstein distance of order p ≥ 1 . Furthermore, for p = 1, we determine the minimax rates of convergence on specific classes of distributions. We finally provide some applications of these results, including the estimation of conditional tail expectation or probability weighted moments.

High Spatial-Resolution and Acquisition-Efficiency Cardiac MR T1 Mapping Based on Radial bSSFP and a Low-Rank Tensor Constraint.

Cardiac T1 mapping is valuable for evaluating myocardial fibrosis, yet its resolution and acquisition efficiency are limited, potentially obscuring visualization of small pathologies. To develop a technique for high-resolution cardiac T1 mapping with a less-than-100-millisecond acquisition window based on radial MOdified Look-Locker Inversion recovery (MOLLI) and a calibrationless space-contrast-coil locally low-rank tensor (SCC-LLRT) constrained reconstruction. Prospective. Sixteen healthy subjects (age 25 ± 3 years, 44% females) and 12 patients with suspected cardiomyopathy (age 57 ± 15 years, 42% females), NiCl2-agar phantom. 3-T, standard MOLLI, radial MOLLI, inversion-recovery spin-echo, late gadolinium enhancement. SCC-LLRT was compared to a conventional locally low-rank (LLR) method through simulations using Normalized Root-Mean-Square Error (NRMSE) and Structural Similarity Index Measure (SSIM). Radial MOLLI was compared to standard MOLLI across phantom, healthy subjects, and patients. Three independent readers subjectively evaluated the quality of T1 maps using a 5-point scale (5 = best). Paired t-test, Wilcoxon signed-rank test, intraclass correlation coefficient analysis, linear regression, Bland-Altman analysis. P < 0.05 was considered statistically significant. In simulations, SCC-LLRT demonstrated a significant improvement in NRMSE and SSIM compared to LLR. In phantom, both radial MOLLI and standard MOLLI provided consistent T1 estimates across different heart rates. In healthy subjects, radial MOLLI exhibited a significantly lower mean T1 (1115 ± 39 msec vs. 1155 ± 36 msec), similar T1 SD (74 ± 14 msec vs. 67 ± 23 msec, P = 0.20), and similar T1 reproducibility (28 ± 18 msec vs. 22 ± 15 msec, P = 0.34) compared to standard MOLLI. In patients, the proposed method significantly improved the sharpness of myocardial boundaries (4.50 ± 0.65 vs. 3.25 ± 0.43), the conspicuity of papillary muscles and fine structures (4.33 ± 0.74 vs. 3.33 ± 0.47), and artifacts (4.75 ± 0.43 vs. 3.83 ± 0.55). The reconstruction time for a single slice was 5.2 hours. The proposed method enables high-resolution cardiac T1 mapping with a short acquisition window and improved image quality. 1 TECHNICAL EFFICACY: Stage 1.

Simple closed-form estimation of a binary latent variable model

Abstract This paper develops a closed-form nonparametric estimator of the conditional distribution function for a binary outcome variable given an unobserved latent variable. This type of function is commonly used in models that involve measurement error and dynamic models with agent-specific unobserved heterogeneity. This paper presents a consistent extremum sieve estimator with the following advantages: i) it has a closed-form expression for all the sieve coefficients; ii) it is computationally straightforward, equivalent to computing eigenvalues and eigenvectors of matrices without the use of iterative optimization algorithms. While as flexible as the sieve maximum likelihood estimator (MLE) previously proposed for this model, our estimator proves computationally simpler. The finite sample properties of the estimator are investigated through a Monte Carlo study, and the developed estimator is applied to a probit model to assess the targeting performance of a social welfare program.

Wages: The Worst Transistor Aging Analysis for Large-scale Analog Integrated Circuits via Domain Generalization

Transistor aging leads to the deterioration of analog circuit performance over time. The worst aging degradation is used to evaluate the circuit reliability. It is extremely expensive to obtain it since several circuit stimuli need to be simulated. The worst degradation collection cost reduction brings an inaccurate training dataset when a machine learning (ML) model is used to fast perform the estimation. Motivated by the fact that there are many similar subcircuits in large-scale analog circuits, in this article we propose Wages to train an ML model on an inaccurate dataset for the worst aging degradation estimation via a domain generalization technique. A sampling-based method on the feature space of the transistor and its neighborhood subcircuit is developed to replace inaccurate labels. A consistent estimation for the worst degradation is enforced to update model parameters. Label updating and model updating are performed alternately to train an ML model on the inaccurate dataset. Experimental results on the very advanced 5 nm technology node show that our Wages can significantly reduce the label collection cost with a negligible estimation error for the worst aging degradations compared to the traditional methods.

Assessing Consistency of Respondent-driven Sampling Estimators by Using Repeated Surveys among People Who Inject Drugs (PWID) in New Jersey.

Respondent-driven sampling (RDS) is widely used to sample populations with higher risk of HIV infection for whom no sampling frames exist. However, few studies have been done to assess the consistency of RDS estimators in real world settings. We conducted an assessment study on the consistency of RDS estimators using data from the National HIV Behavioral Surveillance - People Who Inject Drugs surveys in Newark, New Jersey from 2005 to 2018. Population parameter estimates based on RDS-I, RDS-II, Gile's SS, and HCG were compared longitudinally and cross-sectionally. Population homophily statistics and differential recruitment statistics were estimated and compared. Convergence plots were used for RDS diagnosis. Sensitivity analyses were conducted on population size estimates and seeds biases. By comparing time-insensitive population parameters and population homophily statistics estimated by four RDS estimators, the study found that RDS-II and Gile's SS could provide longitudinally and cross-sectionally consistent estimates and population homophily statistics on gender and sexual orientation. Cross-sectional comparison of time-sensitive population parameter estimates also supported the consistency of RDS-II and Gile's SS. However, RDS-I and HCG did not perform well in those comparisons. In conclusion, RDS estimators may not address all inconsistencies, but RDS-II and Gile's SS are recommended to weight RDS samples given enough consistency was observed in them.

The Efficient Triangle: Export Persistence, Human Capital, and Productivity

ABSTRACT The impact of exports on productivity is a central theme in macroeconomics, explored in both the ‘learning by exporting’ and ‘Kaldor-Verdoorn law’ literature. We utilize a novel dataset of Italian firm microdata to build an empirical model examining the effect of persistent exporting on firm productivity. The model endogenizes a firm's export propensity using a probit specification that enables us to derive consistent estimates of the impact of exporting on productivity. In addition, we use the share of highly educated workers as a proxy of human capital to test the ability of persistently exporting firms to learn. This impact is derived using a biprobit specification, which enables us to endogenize the joint probability of being a continuously exporting firm while highly endowed with a skilled workforce. The results strongly support the hypothesis that persistent exporters show a significant productivity premium compared to occasional exporters. Moreover, the workforce's education level further increases productivity, with results supporting the hypothesis that highly educated shares of a firm's workforce enhance the ability to learn.

Dynamic model in analyzing Indonesia’s agricultural sector’s macroeconomic performance

Indonesia, an emerging archipelagic nation, possesses abundant natural resources spanning marine, land (including forests and water sources), and diverse biological riches. The agricultural sector emerges as a pivotal driver of growth across the country, exhibiting extensive distribution. Consequently, there is an urgent imperative for comprehensive research to bolster and optimize the performance of this sector. This study aims to meticulously analyze and scrutinize macroeconomic variables aimed at enhancing Indonesia’s agricultural sector. Through the utilization of a dynamic panel model, the study zeroes in on crucial variables: economic growth in the agricultural sector, farmer terms of exchange, human development index, population density, inflation, average daily wages, and lagged economic growth data from each province in Indonesia. The best model for dynamic panel testing, employing both First Difference Generalized Method of Moments (FD-GMM) and Generalized Method of Moments System (SYS-GMM) approaches, is identified as the SYS-GMM model. This model exhibits unbiased and consistent estimation, as evidenced by the Arellano-Bond (AB) test and Sargan test results. The analysis conducted using this selected model reveals notable findings. Lagging agricultural sector performance, human capital measured by the Human Development Index (HDI), and farmers’ exchange rates are found to significantly and positively influence the economic growth of the agricultural sector. Conversely, inflation exerts a significant and negative impact on sectoral growth. However, wage levels and population density do not demonstrate a significant partial effect on the economic growth of the agricultural sector.

Clonal hematopoiesis, cardiovascular events and treatment benefit in 63,700 individuals from five TIMI randomized trials.

Clonal hematopoiesis of indeterminate potential (CHIP) has been associated with an increased risk of cardiovascular (CV) disease in the general population. Currently, it is unclear whether this association is observed in large clinical trial cohorts with a high burden of existing CV disease or whether CV therapies can mitigate CHIP-associated CV risk. To address these questions, we studied 63,700 patients from five randomized trials that tested established therapies for CV disease, including treatments targeting the proteins PCSK9, SGLT2, P2Y12 and FXa. During a median follow-up of 2.5 years, 7,453 patients had at least one CV event (CV death, myocardial infarction (MI), ischemic stroke or coronary revascularization). The adjusted hazard ratio (aHR) for CV events for CHIP+ patients was 1.07 (95% CI: 0.99-1.16, P = 0.08), with consistent risk estimates across each component of CV risk. Significant heterogeneity in the risk of MI was observed, such that CHIP+ patients had a 30% increased risk of first MI (aHR = 1.31 (1.05-1.64), P = 0.02) but no increased risk of recurrent MI (aHR = 0.94 (0.79-1.13), Pint = 0.008), as compared to CHIP- patients. Moreover, no significant heterogeneity in treatment effect between individuals with and without CHIP was observed for any of the therapies studied in the five trials. These results indicate that in clinical trial populations, CHIP is associated with incident but not recurrent coronary events and that the presence of CHIP does not appear to identify patients who will derive greater benefit from commonly used CV therapies.

Performance Improvement Approach to Naval Gun Fire Control System Based on Linear Target Tracking Filter with Radar Line-of-sight Measurements

This paper addresses a novel approach to performance enhancement of the naval gun fire control system(FCS) by using the projectile tracking filter without any distortion of radar measurements. Under the assumption that the maneuvering between the projectile and the ship equipped with the radar is not quite large, this method is based on the concept of polar-coordinate target tracking, which separates the range estimation filter and the direction cosine estimation filter. Note that using polar-coordinates allows tracking to be performed in the same coordinate system from which the radar line-of-sight(LOS) measurements are obtained, unlike the conventional tracking process in Cartesian. Also, it is easy to implement in real-time and guarantees consistent estimates due to its linear filter structure. With the help of the above method, therefore, the proposed filter is able to improve the overall performance of FCS which requires stability of projectile estimates within a short engagement time. The effectiveness of the presented scheme is validated through computer simulations.

FITTING DYNAMICALLY CONSISTENT FORWARD RATE CURVES: ALGORITHM AND COMPARISON

This paper introduces a new methodology for estimating dynamically consistent forward rate curves, which are essential for obtaining arbitrage-free valuation and risk management models. Using U.S. Treasury data from January 2013 to June 2023, we fit dynamically consistent forward rates curves and test the goodness of fit between theoretical and observed prices. We compare the traditional static forward rate curve parameterizations (including Nelson–Siegel, Svensson, and cubic splines) with our new dynamically consistent methodology. The dynamically consistent forward rate curve estimates generally outperform the traditional static methods in reproducing market prices.

Assessing heterogeneity in treatment initiation guidelines in longitudinal randomized controlled trials

Treatment initiation guidelines are essential in healthcare, dictating when patients begin therapy. These guidelines are typically assessed through randomized controlled trials (RCTs) to measure their average effect on a population. However, this method may not fully account for patient heterogeneity. We introduce a refined analysis methodology that accounts for diverse times to treatment initiation (TTI) arising from these guidelines. We offer a more detailed perspective on the guidelines’ impact by analyzing homogeneous subpopulations based on their TTI. We develop a longitudinal regression model with smooth time functions to capture dynamic changes in average guideline effects on subpopulations (AGES). A unique weighting mechanism creates pseudo-subpopulations from RCT data, enabling consistent and precise estimation of smooth functions. The efficacy of our approach is validated through theoretical and numerical studies, underscoring its capacity to provide insightful statistical inferences. We exemplify the utility of our methodology by applying it to an RCT of the World Health Organization (WHO) guideline for adults with HIV. This analysis promises to enhance the evaluation of treatment initiation guidelines, leading to more personalized and efficient patient care.

Estimation of density functionals via cross‐validation

AbstractIn density estimation, the mean integrated squared error (MISE) is commonly used as a measure of performance. In that setting, the cross‐validation criterion provides an unbiased estimator of the MISE minus the integral of the squared density. Since the minimum MISE is known to converge to zero, this suggests that the minimum value of the cross‐validation criterion could be regarded as an estimator of minus the integrated squared density. This novel proposal presents the outstanding feature that, unlike all other existing estimators, it does not need the choice of any tuning parameter. Indeed, it is proved here that this approach results in a consistent and efficient estimator, with remarkable performance in practice. Moreover, apart from this base case, it is shown how several other problems on density functional estimation can be similarly handled using this new principle, thus demonstrating full potential for further applications.

How estimating nuisance parameters can reduce the variance (with consistent variance estimation).

We often estimate a parameter of interest when the identifying conditions involve a finite-dimensional nuisance parameter . Examples from causal inference are inverse probability weighting, marginal structural models and structural nested models, which all lead to unbiased estimating equations. This article presents a consistent sandwich estimator for the variance of estimators that solve unbiased estimating equations including which is also estimated by solving unbiased estimating equations. This article presents four additional results for settings where solves (partial) score equations and does not depend on . This includes many causal inference settings where describes the treatment probabilities, missing data settings where describes the missingness probabilities, and measurement error settings where describes the error distribution. These four additional results are: (1) Counter-intuitively, the asymptotic variance of is typically smaller when is estimated. (2) If estimating is ignored, the sandwich estimator for the variance of is conservative. (3) A consistent sandwich estimator for the variance of . (4) If with the true plugged in is efficient, the asymptotic variance of does not depend on whether is estimated. To illustrate we use observational data to calculate confidence intervals for (1) the effect of cazavi versus colistin on bacterial infections and (2) how the effect of antiretroviral treatment depends on its initiation time in HIV-infected patients.