Local Alternatives Research Articles

Model Checking in Partially Linear Spatial Autoregressive Models

This article proposes two new classes of nonparametric tests for the correct specification of linear spatial autoregressive models based on the “integrated conditional moment” approach. Our test statistics are constructed as continuous functionals of a residual marked empirical process as well as its projected version. We derive asymptotic properties of the test statistics under the null hypothesis, the alternative hypothesis, and a sequence of local alternatives. The proposed tests do not involve the selection of tuning parameters such as bandwidths and are able to detect a broad class of local alternatives converging to the null at the parametric rate n − 1 / 2 , with n being the sample size. We also propose a multiplier bootstrap procedure that is computationally simple to approximate the critical values. Monte Carlo simulations illustrate that our tests have a reasonable size and satisfactory power for different types of data-generating processes. Finally, an empirical analysis of growth convergence is carried out to demonstrate the usefulness of the tests.

Codified and reflexive policy coordination: how successful are Dhaka City’s policies for a successful sustainable passenger transport transition?

Abstract Critical to achieving the global net zero objective, transport transitions require multi-domain policies to coordinate and cohere. Different policy domains involve actors with diverse priorities and perspectives, complicating coordination. Policy coordination is particularly challenging for the Global South governments given their dependence for external financial and technological resources, and weaker institutions. Drawing on transport transition and policy coordination literature, this research studied the low-emission transport policies of Dhaka City, Bangladesh from 2000 to 2021. The study found that policy frameworks and institutional arrangements were established to enable local and global actors (development partners) to coordinate to support transport transitions. The development partners were engaged in mobilizing resources, transferring low-emission transport technologies and local capacity development. However, these policies were focused on politically prioritized supply-side solutions (e.g. metro-rail), without sufficient coherence with land use and housing policies. Also, they did not adequately consider local need-based low-emission transport alternatives such as walking and cycling; unintended policy effects and boundary-spanning challenges. Based on the findings, we propose codified and reflexive policy coordination concepts to describe discrete but mutually reinforcing mechanisms to support effective policy coordination. Codified coordination is enacted by formal policy frameworks and institutional arrangements and reflexive coordination is by formal evaluation of policy interventions. This framework proposes a new approach to examine coordination between multi-domain policies that are required to cohere to advance transport transitions. In practice, it can potentially guide the adoption of coordinated policies to drive transport transitions.

Crude oil sorption capacity of modified and unmodified biowaste

Contamination of water by crude oil is ubiquitous particularly in developing countries. The governments of affected countries raise a lot of concern because of the associated economic and environmental impacts which are not in tandem with the United Nations Sustainable Development Goals (SDGs), 6, 8, 11, 13 & 14. Remediation of crude oil-impacted water and soil become imperative. However, the exercise is expensive; hence the search for cheaper and local alternatives. In this study, unsegregated wood sawdust was collected from the various wood saw-dust dump sites within the main Timber shade in the vicinity of Udu Bridge, Udu LGA of Delta State, Nigeria. One half of the sample was chemically modified by acetylation using acetic anhydride while the other half wasn’t. They were characterized using Fourier Transformed Infra-Red Spectrophotometer (FTIR) and Scanning Electron Microscope coupled with Electron Dispersive X-ray (SEM/EDX) analyzer before employing them in the removal of the crude oil from the simulated crude oil spill- water media. The obtained results indicated that the modified sorbent (wood sawdust) sorbed more crude oil than the unmodified sorbent (wood sawdust). Experimental process control factors limited to time, sorbent dosage, particle size, was investigated using the modified sawdust. The modified sorbent gave maximum oil absorption capacity of 5.228 gg-1 for sorption time, 4.759 gg-1 for sorbent dosage and 5.838 gg-1 for particle size analyses. The study contributes to the knowledge of sorption studies using unsegregated wood sawdust for remediation of crude oil-contaminated media.

Reprint: Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic

Mediation analysis draws increasing attention in many research areas such as economics, finance and social sciences. In this paper, we propose new statistical inference procedures for high dimensional mediation models, in which both the outcome model and the mediator model are linear with high dimensional mediators. Traditional procedures for mediation analysis cannot be used to make statistical inference for high dimensional linear mediation models due to high-dimensionality of the mediators. We propose an estimation procedure for the indirect effects of the models via a partially penalized least squares method, and further establish its theoretical properties. We further develop a partially penalized Wald test on the indirect effects, and prove that the proposed test has a χ2 limiting null distribution. We also propose an F-type test for direct effects and show that the proposed test asymptotically follows a χ2-distribution under null hypothesis and a noncentral χ2-distribution under local alternatives. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed tests and compare their performance with existing ones. We further apply the newly proposed statistical inference procedures to study stock reaction to COVID-19 pandemic via an empirical analysis of studying the mediation effects of financial metrics that bridge company’s sector and stock return.

Oracle Efficient Estimation of Heterogeneous Dynamic Panel Data Models with Interactive Fixed Effects

We propose a two-step procedure to estimate a heterogeneous dynamic panel data model with interactive fixed effects. We establish the asymptotic properties of the estimators and show that the final estimator is oracle efficient. We also propose a specification test for the null hypothesis of homogeneous slopes and study the asymptotic properties of the test statistic under both local and global alternatives. Simulations demonstrate the fine performance of the estimator and test statistic. The new estimation and inference methods are applied to study the heterogeneous effects of minimum wage on employment across different counties in the United States. Our dynamic model suggests that the changes of employment range from about–1% to 1% when the minimum wage increases by 1%.

A robust permutation test for subvector inference in linear regressions

We develop a new permutation test for inference on a subvector of coefficients in linear models. The test is exact when the regressors and the error terms are independent. Then we show that the test is asymptotically of correct level, consistent, and has power against local alternatives when the independence condition is relaxed, under two main conditions. The first is a slight reinforcement of the usual absence of correlation between the regressors and the error term. The second is that the number of strata, defined by values of the regressors not involved in the subvector test, is small compared to the sample size. The latter implies that the vector of nuisance regressors is discrete. Simulations and empirical illustrations suggest that the test has good power in practice if, indeed, the number of strata is small compared to the sample size.

Multidimensional biological characteristics of ground glass nodules.

The detection rate of ground glass nodules (GGNs) has increased in recent years because of their malignant potential but relatively indolent biological behavior; thus, correct GGN recognition and management has become a research focus. Many scholars have explored the underlying mechanism of the indolent progression of GGNs from several perspectives, such as pathological type, genomic mutational characteristics, and immune microenvironment. GGNs have different major mutated genes at different stages of development; EGFR mutation is the most common mutation in GGNs, and p53 mutation is the most abundant mutation in the invasive stage of GGNs. Pure GGNs have fewer genomic alterations and a simpler genomic profile and exhibit a gradually evolving genomic mutation profile as the pathology progresses. Compared to advanced lung adenocarcinoma, GGN lung adenocarcinoma has a higher immune cell percentage, is under immune surveillance, and has less immune escape. However, as the pathological progression and solid component increase, negative immune regulation and immune escape increase gradually, and a suppressive immune environment is established gradually. Currently, regular computer tomography monitoring and surgery are the main treatment strategies for persistent GGNs. Stereotactic body radiotherapy and radiofrequency ablation are two local therapeutic alternatives, and systemic therapy has been progressively studied for lung cancer with GGNs. In the present review, we discuss the characterization of the multidimensional molecular evolution of GGNs that could facilitate more precise differentiation of such highly heterogeneous lesions, laying a foundation for the development of more effective individualized treatment plans.

Comparative life cycle assessment of sewage sludge treatment in Wuhan, China: Sustainability evaluation and potential implications

Owing to the relentless growth of sewage sludge production, achieving low-carbon development in sewage sludge treatment and disposal (STD) is becoming increasingly challenging and unpredictable. However, the STD varied spatially, and city-specific analysis is deemed necessary for sustainable evaluation. Therefore, a lifecycle-based greenhouse gas (GHG), energy, and economic analysis were conducted by considering six local STD alternatives in Wuhan City, China, as a case study. The findings indicated anaerobic digestion combined with digestate utilization for urban greening (ADL) and incineration in existing power plants (INCP) exhibited the least GHG emissions at 34.073 kg CO2 eq/FU and 644.128 kg CO2 eq/FU, while INCP generated the most energy at −2594 kW.h/FU. The economic evaluation revealed that ADL and INCP were more beneficial without accounting for land acquisition. Scenario analysis showed that the energy recovery from ADL and INCP is significantly influenced by the hydrolysis yielding rate and sludge organic content. Perturbation sensitivity indicates that regional emission factor of electricity and electricity fee highly influence the overall GHG emission and cost. The results of this study could assist policymakers in identifying viable solutions to the cities experiencing the same sludge treatment burdens.

Remote Surgical Discussion of Multivessel Coronary Artery Disease Patients without Surgery on Site-Retrospective Insights.

The heart team approach is highly advocated for in treatment decision making in patients with multivessel disease (MVD). Nevertheless, many centers lack on-site cardiac surgical services (CSS)/formal heart team. Our local alternative is of remote surgical consultation without a structured image sharing platform. In our understanding, the incidence of anatomical complete revascularization (ACR) under this daily practice, and its clinical impact, has not been discussed before. We analyzed 477 consecutive patients who were surgically revascularized between January 2009 and March 2018 for MVD, after remote surgical consultation. Unstable, late arrival, and ST elevation patients were excluded (n = 163). ACR was considered grafting all anatomic lesions > 50%. Syntax score (SS) calculation and ACR categorization were determined by an independent interventionalist using diagnostic angiograms and available operative reports (n = 267). Patients' outcomes were assessed in relation to multiple clinical variables including troponin result and the revascularization status. Three hundred and fourteen patients were included. Mean age was 64 years, and mean SS-II was 27.3 ± 11. At the 4-year follow-up, the observed mortality (11.8% and 12.9%, with troponin-positive and -negative groups, respectively), myocardial infarction (11.8%), and repeat revascularization (9.8%) were higher than those predicted using a nomogram depicting the predicted 4-year mortality as a function of the SYNTAX II Score (5.3%, 8.8%, and 3.5%, respectively, p = 0.02). ACR was reported in 33% of 267 available patients' reports. After multivariate adjustment ACR was the only variable associated with a significant increase in 4-year mortality (12.3% vs. 6.7%, p < 0.05). Partial revascularization in the absence of on-site CSS and a structured heart team platform is a frequent occurrence. Not surprisingly, this occurrence was associated with a higher risk for mid-term mortality. An upfront, structured, virtual, heart team interface is mandatory to particularly prioritize the completeness of revascularization when considering the optimal revascularization mode.

COVID-19 Impact Analysis: Assessing African Sectors - Commodity, Service, Manufacturing, and Education using Mixed Model Approach

The global onslaught of COVID-19 brought about unforeseen disruptions, significantly imprinting on sectors like essential goods, services, manufacturing, and education. African nations, characterized by theirdistinct socio-economic tapestries, stood at an intriguing juncture—facing both systemic vulnerabilities and demonstrating admirable adaptability. This research delves into the multifaceted impacts experienced by these nations during the global economic turmoil.Our exploration, bolstered by graphical analyses, examines shifts in demand dynamics, particularly contrasting essential and luxury goods. The pandemic inducedphenomena like panic buying, while simultaneously causing economic slowdowns, reshaping consumption patterns. The services sector’s narrative is bifurcated: while traditional services faced setbacks, digitalcounterparts witnessed exponential growth. In manufacturing, disrupted supply chains contrasted with surges in essential goods production. With global trade facing unprecedented challenges, a noticeable tilt towards local alternatives emerges in Least Developed Countries (LDCs). This trend signals both adaptability and a potential pivot towards self-reliance amidst escalating living costs. Moreover, the burgeoning influence oftechnology, particularly Artificial Intelligence (AI), proposes a transformative phase for African education, hinting at enhanced accessibility and quality. However, this optimism is tempered by challenges such as infrastructural gaps and the imperative for improved digital literacy.In Conclusion, this paper provides a concise yet encompassing perspective on the economic reverberations of the COVID-19 pandemic, centering on the unique experiences and lessons from the African landscape.

A global test for heteroscedastic one-way FMANOVA with applications

Multivariate functional data are prevalent in various fields such as biology, climatology, and finance. Motivated by the World Health Data applications, in this study, we propose and examine a global test for assessing the equality of multiple mean functions in multivariate functional data. This test addresses the one-way Functional Multivariate Analysis of Variance (FMANOVA) problem, which is a fundamental issue in the analysis of multivariate functional data. While numerous analysis of variance tests have been proposed and studied for univariate functional data, only a limited number of methods have been developed for the one-way FMANOVA problem. Furthermore, our global test has the ability to handle heteroscedasticity in the unknown covariance function matrices that underlie the multivariate functional data, which is not possible with existing methods. We establish the asymptotic null distribution of the test statistic as a chi-squared-type mixture, which depends on the eigenvalues of the covariance function matrices. To approximate the null distribution, we introduce a Welch–Satterthwaite type chi-squared-approximation with consistent parameter estimation. The proposed test exhibits root-n consistency, meaning it possesses nontrivial power against a local alternative. Additionally, it offers superior computational efficiency compared to several permutation-based tests. Through simulation studies and applications to the World Health Data, we highlight the advantages of our global test.

The distortion in the EU feed market due to import constraints on genetically modified soy

Feed importers in some EU member states face constraints on imports of genetically modified (GM) soy, a practice that may compromise the interests of EU livestock farmers. Using the cases of Sweden and Austria, we analyzed price transmission in the soy supply chain originating from Brazil, applying an asymmetric non-linear auto-regressive distributed lag (ARDL) model to identify short-run and long-run asymmetries. The results revealed significant asymmetric effects in how positive and negative price changes are absorbed within the feed industry. Notably, increases in the cost of Brazilian soy swiftly affect the prices for EU farmers, while cost reductions fail to trigger corresponding price decreases. Consequently, stronger constraints on GM soy imports are likely to exacerbate the competitiveness challenges faced by livestock farmers, primarily due to their reliance on non-GM soy. This implies that the restrictions on GM imports need to be relaxed or that low-cost local protein alternatives need to be developed.

A conditional linear combination test with many weak instruments

We consider a linear combination of jackknife Anderson-Rubin (AR), jackknife Lagrangian multiplier (LM), and orthogonalized jackknife LM tests for inference in IV regressions with many weak instruments and heteroskedasticity. Following I.Andrews (2016), we choose the weights in the linear combination based on a decision-theoretic rule that is adaptive to the identification strength. Under both weak and strong identifications, the proposed test controls asymptotic size and is admissible among certain class of tests. Under strong identification, our linear combination test has optimal power against local alternatives among the class of invariant or unbiased tests which are constructed based on jackknife AR and LM tests. Simulations and an empirical application to Angrist and Krueger’s (1991) dataset confirm the good power properties of our test.

Low-variance Forward Gradients using Direct Feedback Alignment and momentum

Supervised learning in deep neural networks is commonly performed using error backpropagation. However, the sequential propagation of errors during the backward pass limits its scalability and applicability to low-powered neuromorphic hardware. Therefore, there is growing interest in finding local alternatives to backpropagation. Recently proposed methods based on forward-mode automatic differentiation suffer from high variance in large deep neural networks, which affects convergence. In this paper, we propose the Forward Direct Feedback Alignment algorithm that combines Activity-Perturbed Forward Gradients with Direct Feedback Alignment and momentum. We provide both theoretical proofs and empirical evidence that our proposed method achieves lower variance than forward gradient techniques. In this way, our approach enables faster convergence and better performance when compared to other local alternatives to backpropagation and opens a new perspective for the development of online learning algorithms compatible with neuromorphic systems.

Building resilience for an uncertain drinking water future

AbstractEnhancing drinking water resilience has become increasingly important. However, a comprehensive analysis of drinking water emergency countermeasures is lacking. This study evaluated eight countermeasures including monitoring, local alternatives, reclaimed water, interconnection, bulk water, pre‐packaged water, emergency treatment, and isolation valves from resilience and sustainability (i.e., life cycle cost) perspectives. While countermeasures such as interconnections perform relatively well from both perspectives, there is a clear trade‐off between resilience and cost. Local alternatives and emergency treatment respond quickly and provide sustained supply during emergencies but may incur higher costs. Bulk water and pre‐packaged water are typically inexpensive but have limited supply capacity and take time to distribute. As future threats are likely to become more frequent and prolonged, it is prudent for service providers to invest in countermeasures that perform well in both resilience and cost and use an integrated approach that combines high capital projects with bulk/pre‐packaged water contracts.

What is the most effective topical method for preventing presacral pressure sores that occur after hip fractures?

Pressure sores are frequently observed in patients hospitalized due to hip fractures. This condition is caused by pressure, shearing forces, and friction on the skin and subcutaneous tissues. During the preoperative preparation period leading up to surgery, patients often remain immobile due to pain, lie in a supine position, and must use incontinence products because they cannot meet their toileting needs. Incontinence-related dermatitis is observed in patients using incontinence products, which constitutes a fundamental cause for pressure sore development. Since hip fractures are generally seen in the elderly population, advanced age, nutritional disorders, moist skin, infection, and metabolic diseases (e.g., diabetes, kidney failure) are frequently associated, serving as additional factors that facilitate pressure sore formation. Once a pressure sore develops, its treatment is lengthy and costly. Therefore, reducing pressure and addressing risk factors is crucial for prevention and treatment. The choice of treatment methods may vary according to the severity of the wound. In addition to systemic treatments, local topical treatments, including alleviating pressure and shearing forces, wound care, prevention of incontinence-related dermatitis, and nutritional regulation, are essential. Surgical treatment procedures may be necessary in advanced cases.&#x0D; Comparison of local topical treatment alternatives that can be applied before surgical treatment has not been conducted in the literature. This study aims to find the most effective method to address this significant problem by comparing three different materials and a control group applied in our clinic.&#x0D; This study particularly focuses on the topical treatment of presacral pressure sores occurring after hip fractures and is the first and only study in the literature.&#x0D; Presacral pressure sores are frequently observed in hip fractures, and the most effective method for preventing their formation and maceration is the application of barrier cream pads and barrier spray after isotonic cleansing.

Tests for equality of several covariance matrix functions for multivariate functional data

Multivariate functional data are often observed in many scientific fields. This paper considers a multi-sample equal-covariance matrix function testing problem for multivariate functional data. Two new tests are proposed and studied. The asymptotic properties of the two tests under the null hypothesis and a local alternative are investigated. Two methods for approximating the null distributions of the test statistics are described. It is shown that the two tests are root-n consistent. Two simulation studies are conducted to evaluate the finite sample performance of the proposed tests. Finally, the two tests are illustrated via applications to three real multivariate functional data sets.

Injected urbanism: urban theory from India?

ABSTRACT This article reports on an urbanization process that can be described as injected urbanism. While conventional Northern theoretical perspectives capture an important role for one-way rural-to-urban migration of households in urban growth processes, injected urbanism centers on circular labor migration of individuals and remittances flows as crucial drivers of urban growth. Injected urbanism is incipient by nature and geographically specific to predominantly rural regions. Through a ten-point conceptual-analytical framework, I illustrate how this process unfolds in India. Injected urbanism, or locally distinctive versions thereof, can likely be found across broader geographies in the Global South, especially in areas where there is a structural employment shift out of agriculture, few local employment alternatives, and little household out-migration. The article engages with current core theoretical debates in urban geography and furthers the development of postmodern/postcolonial urban viewpoints.

A parametric specification test for linear spatial autoregressive models

We propose a new test for the specification of linear spatial autoregressive models where the spatial weights matrix is prespecified. Our test is built on the difference of two estimates of the spatial parameter where the two estimates are obtained by the parametric and nonparametric GMM estimation methods, respectively. Under mild assumptions, we derive the limiting null distribution and show consistency for our test. Unlike the general nonparametric test, our test can detect the local alternatives that approach the null at a rate n−1/2, where n is the sample size. Monte Carlo simulations are conducted to study the finite sample performance of our test. Finally, we apply our test to check the model specification for the economic growth rate example.

Significance test for semiparametric conditional average treatment effects and other structural functions

The paper investigates a hypothesis testing problem concerning the potential additional contributions of other covariates to the structural function, given the known covariates. The structural function is the conditional expectation given covariates in which the response may depend on unknown nuisance functions. It includes classic regression functions and the conditional average treatment effects as illustrative instances. Based on Neyman's orthogonality condition, the proposed distance-based test exhibits the quasi-oracle property in the sense that the nuisance function asymptotically does not influence on the limiting distributions of the test statistic under both the null and alternatives. This novel test can effectively detect the local alternatives distinct from the null at the fastest possible rate in hypothesis testing. This is particularly noteworthy given the involvement of nonparametric estimation of the conditional expectation. Numerical studies are conducted to examine the performance of the test. In the real data analysis section, the proposed tests are applied to identify significantly explanatory covariates that are associated with AIDS treatment effects, yielding noteworthy insights.