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Coordinate-based meta-analyses (CBMA) are very useful for summarizing the large number of voxel-based neuroimaging studies of normal brain functions and brain abnormalities in neuropsychiatric disorders. However, current CBMA methods do not conduct common voxelwise tests, but rather a test of convergence, which relies on some spatial assumptions that data may seldom meet, and has lower statistical power when there are multiple effects. Here we present a new algorithm that can use standard voxelwise tests and, importantly, conducts a standard permutation of subject images (PSI). Its main steps are: a) multiple imputation of study images; b) imputation of subject images; and c) subject-based permutation test to control the familywise error rate (FWER). The PSI algorithm is general and we believe that developers might implement it for several CBMA methods. We present here an implementation of PSI for seed-based d mapping (SDM) method, which additionally benefits from the use of effect sizes, random-effects models, Freedman-Lane-based permutations and threshold-free cluster enhancement (TFCE) statistics, among others. Finally, we also provide an empirical validation of the control of the FWER in SDM-PSI, which showed that it might be too conservative. We hope that the neuroimaging meta-analytic community will welcome this new algorithm and method. •We present a new algorithm for coordinate-based meta-analyses (CBMA) methods.•Opposed to current methods, it conducts common permutation tests.•It may be implemented in several CBMA methods.•We detail and validate its implementation for seed-based d mapping (SDM).

Coordinate-based meta-analyses (CBMA) methods, such as Activation Likelihood Estimation (ALE) and Seed-based d Mapping (SDM), have become an invaluable tool for summarizing the findings of voxel-based neuroimaging studies. However, the progressive sophistication of these methods may have concealed two particularities of their statistical tests. Common univariate voxelwise tests (such as the t/z-tests used in SPM and FSL) detect voxels that activate, or voxels that show differences between groups. Conversely, the tests conducted in CBMA test for “spatial convergence” of findings, i.e., they detect regions where studies report “more peaks than in most regions”, regions that activate “more than most regions do”, or regions that show “larger differences between groups than most regions do”. The first particularity is that these tests rely on two spatial assumptions (voxels are independent and have the same probability to have a “false” peak), whose violation may make their results either conservative or liberal, though fortunately current versions of ALE, SDM and some other methods consider these assumptions. The second particularity is that the use of these tests involves an important paradox: the statistical power to detect a given effect is higher if there are no other effects in the brain, whereas lower in presence of multiple effects. •The statistical tests of coordinate-based meta-analyses (CBMA) have particularities.•Differently from what common voxelwise tests do, they test for spatial convergence.•Violation of their spatial assumptions may make results either conservative or liberal.•They have lower statistical power in the presence of multiple effects.

The purpose of the research is based on the spatial network correlation to explore the convergence path of inclusive green growth. Inclusive green growth is a sustainable development model that emphasizes the integration of economic, social, and ecological systems. Based on the three subsystems of economic growth, social inclusion, and green sustainability, this study structures the indicator system of China’s inclusive green growth and reveals the characteristics of China’s inclusive green growth network using the social network analysis (SNA) method. Then, from the perspective of system deconstruction, this work tests whether and how China’s inclusive green growth converges by constructing a spatial econometric model with different subsystems of spatial correlation networks as spatial weights. The results show that: (1) China’s inclusive green growth level is on the rise in general, showing a spatial distribution pattern of “high in East and West, low in the Central”. (2) China’s inclusive green growth network relationship is significant, and the network system is stable, but there is still room for improvement in network relevance. The spatial correlation of economic growth is relatively closer than other subsystems. (3) China’s inclusive green growth has a remarkable convergence trend in the spatial correlation scenario, and the spatial correlation of social inclusion has the most significant promoting effect on the convergence of the national inclusive green growth; there is a trend of club convergence in the East, Central, and West regions, and the speed of convergence is the fastest in the central region. The spatial correlation of economic growth has a strong promoting effect on the convergence of inclusive green growth in all regions.

Disentangling ecological processes that influence community assembly and species diversity across spatial scales remains a major goal of community ecology. Community assembly processes influence spatial patterns of species diversity through their interactions with key functional traits. Hence, quantifying spatial patterns of functional trait diversity (FD) represents a useful tool for disentangling the relative importance of abiotic filtering, biotic interactions, random assembly and dispersal limitation across spatial scales. Here, we measured 12 traits of 112 study species in a 20‐ha fully mapped subtropical forest plot. The individuals of the 112 study species account for 99% of all living stems with diameter at breast height ≥ 1 cm. We studied important functional traits related to physiological processes of plants including resource acquisition (e.g. CO2 assimilation rate and leaf nutrient concentration) and drought tolerance (e.g. stem hydraulic conductivity and leaf turgor loss point). Additionally, species abundance, spatial locations (x‐ and y‐coordinates for each individual of the 112 study species) as well as topographic and soil variables that represent potentially important attributes of the physical environment of the plot were also included in our dataset. We employed two FD‐based tests (comparing FD within communities to those from random communities, distance‐based Moran's eigenvector maps (MEM) and redundancy analysis‐based variance partitioning), and one spatial analysis (inhomogeneous bivariate pair correlation analysis) to quantify the spatial patterns of FD of the plot at multiple spatial scales (400, 900, 1,600, 2,500 and 10,000 m2). We demonstrate that abiotic filtering is the major determinant responsible for trait convergence at relatively small scales (400, 900 and 1,600 m2), whereas dispersal limitation becomes dominant, causing the weakening of trait convergence at relatively large scales (2,500 and 10,000 m2). Our results highlight the relative contributions of different ecological processes to community assembly at different spatial scales, which can be distinguished using the diversity patterns of key functional traits. Also, our integrated approaches constitute a useful study design to disentangle variable ecological processes in shaping community assembly across spatial scales. A plain language summary is available for this article. Plain Language Summary

The purpose of this paper is to clarify the convergence pattern of China’s regional economies, explore the driving force of their coordinated development, and provide policy suggestions for coordinated and high-quality development. We used nighttime light data from 1992 to 2020 and combined an exploratory spatial data analytical method and a log-t test of a nonlinear time-varying factor model to identify the spatial convergence clubs of regional economic growth and the economic growth drivers of different clubs based on a spatial econometric model. We found that the eastern region is strong while the development of the central, western, and northeastern regions follows China’s long-term trend. Three high-level economic clubs (Shanghai, Jiangsu, and Zhejiang belong to Club 1; Shandong, Hebei, Anhui, Henan, and Liaoning belong to Club 2; Hainan, Fujian, and Guangdong belong to Club 3) have formed in the eastern coastal and central regions, while a low-level one (Inner Mongolia, Hubei, Chongqing, Qinghai, Guizhou, Sichuan, Guangxi, Yunnan, Xizang, Shaanxi, Gansu, Hunan, Ningxia, Xinjiang, Jiangxi, Heilongjiang, and Jilin) has formed in the central, western, and northeastern regions. Beijing, Tianjin, and Shanxi are not convergent. The coordinated development of these regions requires improving the levels of economic growth in the western and northeastern regions to give full play to the role of the Yangtze River Delta as a growth pole and its economic radiation capacity. An analysis of the influence mechanism and spatial spillover effects shows that industrial development and market vitality are the most important driving forces for economic growth. For the low-level club, service industry development, human capital, and resource consumption are also key factors for achieving sustained and stable economic growth.

We develop a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or, equivalently, the value of a linear programming problem with a potentially infinite constraint set. We show that many bounds characterizations in econometrics, for instance bounds on parameters under conditional moment inequalities, can be formulated as intersection bounds. Our approach is especially convenient for models comprised of a continuum of inequalities that are separable in parameters, and also applies to models with inequalities that are nonseparable in parameters. Since analog estimators for intersection bounds can be severely biased in finite samples, routinely underestimating the size of the identified set, we also offer a medianbias-corrected estimator of such bounds as a by-product of our inferential procedures. We develop theory for large sample inference based on the strong approximation of a sequence of series or kernel-based empirical processes by a sequence of \"penultimate\" Gaussian processes. These penultimate processes are generally not weakly convergent, and thus are non-Donsker. Our theoretical results establish that we can nonetheless perform asymptotically valid inference based on these processes. Our construction also provides new adaptive inequality/moment selection methods. We provide conditions for the use of nonparametric kernel and series estimators, including a novel result that establishes strong approximation for any general series estimator admitting linearization, which may be of independent interest.

Considering the importance of characterizing groundwater flow for assessing recharge and contaminant transport, this study investigates the potential of two field methods to estimate groundwater fluxes in consolidated aquifers. To accomplish this, both the finite volume point dilution method (FVPDM) test and active distributed temperature sensing (Active-DTS) measurements were conducted in a single piezometer in a chalk aquifer. The FVPDM is a single-well tracer experiment, that provides a measurement of the groundwater flow rate across the tested piezometer. Whereas the Active-DTS method was performed by deploying a fiber-optic (FO) cable outside the piezometer within the gravel filter. The Active-DTS method provided high spatial resolution and local groundwater flux estimates along the heated section. Numerical simulations were used to assess the distortion of the groundwater flow field induced by the presence of the well, demonstrating that the groundwater flux is maximum within the well screen, where the FVPDM test was conducted. In the immediate vicinity of the well, where the heated FO cable was installed, the groundwater flux is lower, and the flow pattern consisted of converging and diverging flow lines. Therefore, the position of the heated FO cable relative to the flow direction is critical and can have a significant impact on the estimation of the groundwater flux. Thus, even if the deployment of the FO cable within the gravel pack minimizes convective effects and opens up interesting perspectives to estimate vertical heterogeneities, this approach may be limited if the position of the FO cable relative to the flow direction is not well known.

Motivated by recent work on studying massive imaging data in various neuroimaging studies, we propose a novel spatially varying coefficient model (SVCM) to capture the varying association between imaging measures in a three-dimensional volume (or two-dimensional surface) with a set of covariates. Two stylized features of neuorimaging data are the presence of multiple piecewise smooth regions with unknown edges and jumps and substantial spatial correlations. To specifically account for these two features, SVCM includes a measurement model with multiple varying coefficient functions, a jumping surface model for each varying coefficient function, and a functional principal component model. We develop a three-stage estimation procedure to simultaneously estimate the varying coefficient functions and the spatial correlations. The estimation procedure includes a fast multiscale adaptive estimation and testing procedure to independently estimate each varying coefficient function, while preserving its edges among different piecewise-smooth regions. We systematically investigate the asymptotic properties (e.g., consistency and asymptotic normality) of the multiscale adaptive parameter estimates. We also establish the uniform convergence rate of the estimated spatial covariance function and its associated eigenvalues and eigenfunctions. Our Monte Carlo simulation and real-data analysis have confirmed the excellent performance of SVCM. Supplementary materials for this article are available online.

Economic development and ongoing urbanization are usually accompanied by severe haze pollution. Revealing the spatial and temporal evolution of haze pollution can provide a powerful tool for formulating sustainable development policies. Previous studies mostly discuss the differences in the level of PM2.5 among regions, but have paid little attention to the change rules of such differences and their clustering patterns over long periods. Therefore, from the perspective of club convergence, this study employs the log t regression test and club clustering algorithm proposed by Phillips and Sul (Econometrica 75(6):1771–1855, 2007. 10.1111/j.1468-0262.2007.00811.x) to empirically examine the convergence characteristics of PM2.5 concentrations in Chinese cities from 1998 to 2016. This study found that there was no evidence of full panel convergence, but supported one divergent group and eleven convergence clubs with large differences in mean PM2.5 concentrations and growth rates. The geographical distribution of these clubs showed significant spatial dependence. In addition, certain meteorological and socio-economic factors predominantly determined the convergence club for each city.

The aim of this study is to conduct a systematic and theoretical analysis of estimations and inferences for a class of functional mixed-effects models (FMEM). FMEMs consist of fixed effects that characterize the association between longitudinal functional responses and covariates of interest and random effects that capture the spatial-temporal correlations of longitudinal functional responses. We propose local linear estimates of refined fixed-effect functions and establish their weak convergence, along with a simultaneous confidence band for each fixed-effect function. We propose a global test for the linear hypotheses of varying coefficient functions and derive the associated asymptotic distribution under the null hypothesis and the asymptotic power under the alternative hypothesis. We also establish the convergence rates of the estimated spatial-temporal covariance operators and their associated eigenvalues and eigenfunctions. We conduct extensive simulations and apply our method to a white-matter fiber data set from a national database for autism research to examine the finite-sample performance of the proposed estimation and inference procedures.