arXiv subjects

Li Tuobang

Publications and source records attributed to Li Tuobang.

10 recordsLinked to original sources

arXiv2026

Implementing Substance Over Form: A Novel Metric for Taxing E-commerce to Address Deterritorialization

Against the backdrop of e-commerce restructuring consumption patterns, last-mile delivery stations have substantially fulfilled the function of community retail distribution. However, the current tax system only levies a low labor service tax on delivery fees, resulting in a tax contribution from the massive circulating goods value that is significantly lower than that of retail supermarkets of equivalent scale. This disparity not only triggers local tax base erosion but also fosters unfair competition. Based on the "substance over form" principle, this paper proposes a tax rate calculation method using "delivery fee plus insurance premium" as the base, corrected through "goods value conversion." This method aims to align the substantive tax burden of e-commerce with that of community retail at the terminal stage, effectively internalizing the high negative externalities of delivery stations through fiscal instruments, addressing E-commerce Deterritorialization.

stat.AP↗

arXiv2026

The Collapse of Multilayer Predation and the Emergence of a Monolithic Leviathan

This paper constructs a multilayer recursive game model to demonstrate that in a rule vacuum environment, hierarchical predatory structures inevitably collapse into a monolithic political strongman system due to the conflict between exponentially growing rent dissipation and the rigidity of bottom-level survival constraints. We propose that the rise of a monolithic political strongman is essentially an "algorithmic entropy reduction" achieved through forceful means by the system to counteract the "informational entropy increase" generated by multilayer agency. However, the order gained at the expense of social complexity results in the stagnation of social evolutionary functions.

physics.soc-ph↗

arXiv2026

On the Anchoring Effect of Monetary Policy on the Labor Share of Income and the Rationality of Its Setting Mechanism

Modern macroeconomic monetary theory suggests that the labor share of income has effectively become a core macroe-conomic parameter anchored by top policymakers through Open Market Operations (OMO). However, the setting of this parameter remains a subject of intense economic debate. This paper provides a detailed summary of these controversies, analyzes the scope of influence exerted by market agents other than the top policymakers on the labor share, and explores the rationality of its setting mechanism.

stat.AP↗

arXiv2026

The Maintenance and Necessity of Universal Rules: Scale, Hierarchy, the Cost of Justice, and Civilizational Development

Building upon previous research, this paper further explores the topological foundations for maintaining universal rules within ultra-large-scale societies. It finds that in small-scale societies, absolute egalitarianism and the rule of law can be compatible through peer monitoring within a fully connected network. However, in ultra-large-scale societies, to maintain high-dimensional rules capable of protecting innovation and property rights, a complex hierarchical structure including "high-fragility" nodes must be constructed. Through quantitative analysis of power structures, this paper proves that a flattened, two-tier structure inevitably leads to the degradation of the rule of law. Only a social topology with sufficient hierarchical depth can escape the deathly trap of the Leviathan while expanding in scale, thereby sustaining the dynamic evolution of civilization.

physics.soc-ph↗

arXiv2024

Matrix dissimilarities based on differences in moments and sparsity

Generating a dissimilarity matrix is typically the first step in big data analysis. Although numerous methods exist, such as Euclidean distance, Minkowski distance, Manhattan distance, Bray Curtis dissimilarity, Jaccard similarity and Dice dissimilarity, it remains unclear which factors drive dissimilarity between groups. In this paper, we introduce an approach based on differences in moments and sparsity. We show that this method can delineate the key factors underlying group differences. For example, in biology, mean dissimilarity indicates differences driven by up down regulated gene expressions, standard deviation dissimilarity reflects the heterogeneity of response to treatment, and sparsity dissimilarity corresponds to differences prompted by the activation silence of genes. Through extensive reanalysis of genome, transcriptome, proteome, metabolome, immune profiling, microbiome, and social science datasets, we demonstrate insights not captured in previous studies. For instance, it shows that the sparsity dissimilarity is as effective as the mean dissimilarity in predicting the alleviation effects of a COVID 19 drug, suggesting that sparsity dissimilarity is highly meaningful.

q-bio.QM↗

arXiv2024

Robust estimations from distribution structures: III. Invariant Moments

Descriptive statistics for parametric models are currently highly sensative to departures, gross errors, and/or random errors. Here, leveraging the structures of parametric distributions and their central moment kernel distributions, a class of estimators, consistent simultanously for both a semiparametric distribution and a distinct parametric distribution, is proposed. These efficient estimators are robust to both gross errors and departures from parametric assumptions, making them ideal for estimating the mean and central moments of common unimodal distributions. This article opens up the possibility of utilizing the common nature of probability models to construct near-optimal estimators that are suitable for various scenarios.

math.ST↗

arXiv2024

Robust estimations from distribution structures: II. Central Moments

In descriptive statistics, $U$-statistics arise naturally in producing minimum-variance unbiased estimators. In 1984, Serfling considered the distribution formed by evaluating the kernel of the $U$-statistics and proposed generalized $L$-statistics which includes Hodges-Lehamnn estimator and Bickel-Lehmann spread as special cases. However, the structures of the kernel distributions remain unclear. In 1954, Hodges and Lehmann demonstrated that if $X$ and $Y$ are independently sampled from the same unimodal distribution, $X-Y$ will exhibit symmetrical unimodality with its peak centered at zero. Building upon this foundational work, the current study delves into the structure of the kernel distribution. It is shown that the $\mathbf{k}$th central moment kernel distributions ($\mathbf{k}>2$) derived from a unimodal distribution exhibit location invariance and is also nearly unimodal with the mode and median close to zero. This article provides an approach to study the general structure of kernel distributions.

math.ST↗

arXiv2024

Robust estimations from distribution structures: I. Mean

As the most fundamental problem in statistics, robust location estimation has many prominent solutions, such as the trimmed mean, Winsorized mean, Hodges Lehmann estimator, Huber M estimator, and median of means. Recent studies suggest that their maximum biases concerning the mean can be quite different, but the underlying mechanisms largely remain unclear. This study exploited a semiparametric method to classify distributions by the asymptotic orderliness of quantile combinations with varying breakdown points, showing their interrelations and connections to parametric distributions. Further deductions explain why the Winsorized mean typically has smaller biases compared to the trimmed mean; two sequences of semiparametric robust mean estimators emerge, particularly highlighting the superiority of the median Hodges Lehmann mean. This article sheds light on the understanding of the common nature of probability distributions.

math.ST↗

arXiv2024

Robust estimations from distribution structures: V. Non-asymptotic

Due to the complexity of order statistics, the finite sample behaviour of robust statistics is generally not analytically solvable. While the Monte Carlo method can provide approximate solutions, its convergence rate is typically very slow, making the computational cost to achieve the desired accuracy unaffordable for ordinary users. In this paper, we propose an approach analogous to the Fourier transformation to decompose the finite sample structure of the uniform distribution. By obtaining sets of sequences that are consistent with parametric distributions for the first four sample moments, we can approximate the finite sample behavior of other estimators with significantly reduced computational costs. This article reveals the underlying structure of randomness and presents a novel approach to integrate multiple assumptions.

stat.ME↗

arXiv2024

Infer metabolic velocities from moment differences of molecular weight distributions

Metabolic pathways are fundamental maps in biochemistry that detail how molecules are transformed through various reactions. The complexity of metabolic network, where a single compound can play a part in multiple pathways, poses a challenge in inferring metabolic balance changes over time or after different treatments. Isotopic labeling experiment is the standard method to infer metabolic flux, which is currently defined as the flow of a single metabolite through a given pathway over time. However, there is still no way to accurately infer the metabolic balance changes after different treatments in an experiment. This study introduces a different concept: molecular weight distribution, which is the empirical distribution of the molecular weights of all metabolites of interest. By estimating the differences of the location and scale estimates of these distributions, it becomes possible to quantitatively infer the metabolic balance changes even without requiring knowledge of the exact chemical structures of these compounds and their related pathways. This research article provides a mathematical framing for a classic biological concept.

q-bio.QM↗