在标尺回归上对高维共变的正确确规范化估计.
Jie He1, Yumou Qiu2,3, Xiao-Hua Zhou4,5
1School of Mathematics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Biometrics
|March 8, 2025
概括
本研究引入了一种新的规范化方法来建模复杂的高维共变矩阵,解决主体共变量的异质性. 这种方法确保了稀疏性和积极的确定性,这对于稳健的统计分析至关重要.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 神经科学是一个神经科学.
背景情况:
- 协差量测量边际依赖性,但对高维,异质协差的建模具有挑战性.
- 现有的方法与共变量矩阵的大参数空间和正确定性约束作斗争.
研究的目的:
- 为协差的回归系数提出一个规范化的估计方法.
- 为了解决条件平均协差矩阵中正确度的约束.
- 开发一个估计器,同时实现稀疏性和正确性.
主要方法:
- 一种对协差的回归系数进行规范化的估计方法.
- 纳入足够和必要的约束,以获得正确的确定性.
- 一种用于解决优化问题的乘数 (ADMM) 算法的交替方向方法.
主要成果:
- 建议的估计器既满足稀疏性,也满足正定义性.
- 证明了ADMM算法的收.
- 对于回归系数和异质共差的收率是导出的.
结论:
- 这种新的方法有效地模拟了高维,异构的共差.
- ADMM算法为受约束优化问题提供了强大的解决方案.
- 该方法通过模拟和大脑连接案例研究来验证.
相关概念视频
Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Residuals and Least-Squares Property
7.2K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.2K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
324
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
On...
324
Estimating Population Mean with Unknown Standard Deviation
7.6K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
7.6K
Multiple Regression
2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
Estimating Population Mean with Known Standard Deviation
8.2K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.2K


