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群模型中位置与回归参数的 M 估计：Stiefel 流形案例AI 解读

We discuss here a general approach to the calculation of the asymptotic covariance of $M$-estimates for location parameters in statistical group models when an invariant objective function is used. The calculation reduces to standard tools in group representation theory and the calculation of some constants. Only the constants depend upon the precise forms of the density or of the objective function. If the group is sufficiently large this represents a major simplification…

标准化影响矩阵及其应用AI 解读

In this article we introduce the standardized influence matrix (SIM) of parameter estimators as a conjugate of the sample covariance matrix of standardized influence functions (SIFs) evaluated at the data points. We propose principal component analysis for the SIM and its complement (SIM analysis) as a diagnostic tool for regression or other statistical inference, and provide theoretical insight to the local influence defined by Cook. We show that SIM analysis reveals the…

刚体运动的 bootstrap 置信域估计AI 解读

This article deals with statistical inference of the motion of rigid bodies using bootstrap methodology. We consider two types of motion: motion in p dimensional Euclidean space, and motion on a p-dimensional sphere. This article is motivated by problems of interest to polar scientists understanding the motion of ice pack at the North Pole and those to geoscientists studying the motion of tectonic plates. In addition to obtaining the point estimates for various parameters…

球面与欧氏空间中刚体运动的M估计AI 解读

This paper calculates the influence functions and asymptotic distributions of $M$-estimators of the rotation $A$ in a spherical regression model on the unit sphere in $p$ dimensions with isotropic errors. The problem arises in the reconstruction of the motion of a rigid body on the surface of the sphere. The comparable model for $p$-dimensional Euclidean space data is that $(\nu_1, \ldots, \nu_n)$ are independent with $\nu_i$ symmetrically distributed around $\gamma A \cdot u_i + b, u_i$…

含变量测量误差的球面回归AI 解读

Suppose $u_1, \cdots, u_n, v_1, \cdots, v_n$ are random points on the sphere such that for unknown points $\xi_1, \cdots, \xi_n$ and unknown rotation $A_0$, the distribution of $u_i$ depends only on $u^t_i\xi_i$ and that of $v_i$ on $v^t_iA_0\xi_i$. This paper provides asymptotic tests and confidence regions for $A_0$ and for its axis of rotation. Results are given in arbitrary dimension.

根据边界穿越点估计两个构造板块的相对旋转AI 解读

Let η1, …, ηs be unknown vectors on the sphere and Ao be an unknown rotation. Suppose that uij are estimates of points lying on the great circle normal to ηi and υik are estimates of points lying on the great circle normal to Aoηi . This article discusses a method to construct a confidence region for Ao . This problem arises in the reconstruction of the relative motion of two tectonic plates on opposing sides of a rift. The boundary on each side is represented by a…

球面回归AI 解读

Suppose $u_1,\cdots,u_n$ are fixed points on the sphere, $v_1,\cdots,v_n$ are random points such that the distribution of $v_i$ depends only upon $v^t_iAu_i$ for some unknown rotation A. This paper provides asymptotic tests and confidence regions for A and for the axis of rotation of A. Results are given in arbitrary dimension.