On the Least Squares Estimation of Multiple-Threshold-Variable Autoregressive Models

Most threshold models to-date contain a single threshold variable. However, in many empirical applications, models with multiple threshold variables may be needed and are the focus of this paper. For the sake of readability, we start with the two-threshold-variable autoregressive (2-TAR) model and study its least squares estimation (LSE). Among others, we show that the respective estimated thresholds are asymptotically independent. We propose a new method, namely the weighted Nadaraya-Watson method, to construct confidence intervals for the threshold parameters, that turns out to be, as far as we know, the only method to-date that enjoys good probability coverage, regardless of whether the threshold variables are endogenous or exogenous. Finally, we describe in some detail how our results can be extended to the K-threshold-variable autoregressive (K-TAR) model, K > 2. We assess the finite-sample performance of the LSE by simulation and present two real examples to illustrate the efficacy of our modelling.</p

Silver-Catalyzed Cyclopropanation of Alkenes Using <i>N</i>‑Nosylhydrazones as Diazo Surrogates

An efficient silver-catalyzed [2 + 1] cyclopropanation of sterically hindered internal alkenes with diazo compounds in which room-temperature-decomposable <i>N</i>-nosylhydrazones are used as diazo surrogates is reported. The unexpected unique catalytic activity of silver was ascribed to its dual role as a Lewis acid activating alkene substrates and as a transition metal forming silver carbenoids. A wide range of internal alkenes, including challenging diarylethenes, were suitable for this protocol, thereby affording a variety of cyclopropanes with high efficiency in a stereoselective manner under mild conditions

Study on Equity and Efficiency of Health Resources and Services Based on Key Indicators in China

<div><p>Background</p><p>This study aims to evaluate the dialectical relationship between equity and efficiency of health resource allocation and health service utilization in China.</p><p>Methods</p><p>We analyzed the inequity of health resource allocation and health service utilization based on concentration index (CI) and Gini coefficient. Data envelopment analysis (DEA) was used to evaluate the inefficiency of resource allocation and service utilization. Factor Analysis (FA) was used to determine input/output indicators.</p><p>Results</p><p>The CI of Health Institutions, Beds in Health Institutions, Health Professionals and Outpatient Visits were -0.116, -0.012, 0.038, and 0.111, respectively. Gini coefficient for the 31 provinces varied between 0.05 and 0.43; out of these 23 (742%) were observed to be technically efficient constituting the “best practice frontier”. The other 8 (25.8%) provinces were technically inefficient.</p><p>Conclusions</p><p>Health professionals and outpatient services are focused on higher income levels, while the Health Institutions and Beds in Health Institutions were concentrated on lower income levels. In China, a few provinces attained a basic balance in both equity and efficiency in terms of current health resource and service utilization, thus serving as a reference standard for other provinces.</p></div

The eastern, central and western regions of China.

<p>The eastern, central and western regions of China.</p

Nitrogen and Carbon Co-Doped CoO_<i>x</i> Nanostructures for Oxygen Reduction

Although precious metal Pt-based cathodic nanomaterials are extensively considered as the excellent electrocatalysts for overcoming sluggish kinetics in oxygen reduction reaction (ORR), their practical applications are severely impeded by high price, low earth abundance, and poor durability of Pt. Hence, it is highly desired to explore cheaper alternatives with comparable catalytic activity and durability for ORR. Herein, the high-efficiency Co–N–C catalyst, composed of CoOx and N-doped carbon, is successfully constructed by pyrolysis of bimetallic CoZn microspheres and polydopamine (PDA). The N-rich PDA could confer plentiful well-distributed Co–Nx, pyridinic N active sites, and abundant hierarchical pores to the catalyst during pyrolysis. The optimal NC/CoN-2 catalyst manifests an E1/2 of 0.818 V and a JL of 5.27 mA/cm2, as well as excellent MeOH tolerance, long-time durability, and the 4e– reaction pathway in alkaline media. Such a remarkable ORR performance of NC/CoN-2 is attributed to the simultaneous compositional (Co/N co-doping) and structural tailoring (micropore/mesopore/macropore), enabled by PDA surface coating

A model averaging approach for the ordered probit and nested logit models with applications

<p>This paper considers model averaging for the ordered probit and nested logit models, which are widely used in empirical research. Within the frameworks of these models, we examine a range of model averaging methods, including the jackknife method, which is proved to have an optimal asymptotic property in this paper. We conduct a large-scale simulation study to examine the behaviour of these model averaging estimators in finite samples, and draw comparisons with model selection estimators. Our results show that while neither averaging nor selection is a consistently better strategy, model selection results in the poorest estimates far more frequently than averaging, and more often than not, averaging yields superior estimates. Among the averaging methods considered, the one based on a smoothed version of the Bayesian Information criterion frequently produces the most accurate estimates. In three real data applications, we demonstrate the usefulness of model averaging in mitigating problems associated with the ‘replication crisis’ that commonly arises with model selection.</p

Gini coefficient, overall, technical and scale efficiency scores and returns to scale characteristics for health resources allocation and health services utilization of each province.

<p>Numbers in cells show the coefficient of each province for each index. * IRS: increasing return to scale. † DRS: decreasing return to scale.</p

A Mallows-type Model Averaging Estimator for the Varying-Coefficient Partially Linear Model

<p>In the last decade, significant theoretical advances have been made in the area of frequentist model averaging (FMA); however, the majority of this work has emphasised parametric model setups. This paper considers FMA for the semiparametric varying-coefficient partially linear model (VCPLM), which has gained prominence to become an extensively used modeling tool in recent years. Within this context, we develop a Mallows-type criterion for assigning model weights and prove its asymptotic optimality. A simulation study and a real data analysis demonstrate that the FMA estimator that arises from this criterion is vastly preferred to information criterion score-based model selection and averaging estimators. Our analysis is complicated by the fact that the VCPLM is subject to uncertainty arising not only from the choice of covariates, but also whether the covariate should enter the parametric or nonparametric parts of the model.</p

Additional file 1: of Reproductive outcomes in women with unicornuate uterus undergoing in vitro fertilization: a nested case-control retrospective study

Table S1. Reproductive outcomes - comparison between fresh and frozen-thaw ET cycles in each subgroup. (DOCX 20 kb

Table_1_Fatty acid metabolism is related to the immune microenvironment changes of gastric cancer and RGS2 is a new tumor biomarker.xlsx

BackgroundAlterations in lipid metabolism promote tumor progression. However, the role of lipid metabolism in the occurrence and development of gastric cancer have not been fully clarifiedMethodHere, genes that are related to fatty acid metabolism and differentially-expressed between normal and gastric cancer tissues were identified in the TCGA-STAD cohort. The intersection of identified differentially-expressed genes with Geneset was determined to obtain 78 fatty acid metabolism-related genes. The ConsensusClusterPlus R package was used to perform differentially-expressed genes, which yielded divided two gastric cancer subtypes termed cluster 1 and cluster 2.ResultsPatients in cluster 2 was found to display poorer prognosis than patients in cluster 1. Using machine learning method to select 8 differentially expressed genes among subtypes to construct fatty acid prognostic risk score model (FARS), which was found to display good prognostic efficacy. We also identified that certain anticancer drugs, such as bortezomib, elesclomol, GW843682X, and nilotinib, showed significant sensitivity in the high FARS score group. RGS2 was selected as the core gene upon an analysis of the gastric cancer single-cell, and Western blotting and immunofluorescence staining results revealed high level of expression of this gene in gastric cancer cells. The results of immunohistochemical staining showed that a large amount of RGS2 was deposited in the stroma in gastric cancer. A pan-cancer analysis also revealed a significant association of RGS2 with TMB, TIDE, and CD8+ T-cell infiltration in other cancer types as well. RGS2 may thus be studied further as a new target for immunotherapy in future studies on gastric cancer.ConclusionIn summary, the FARS model developed here enhances our understanding of lipid metabolism in the TME in gastric cancer, and provides a theoretical basis for predicting tumor prognosis and clinical treatment.</p