804 research outputs found
Robust Estimation of Multiple Regression Model with asymmetric innovations and Its Applicability on Asset Pricing Model
In this paper, we first develop the modified maximum likelihood (MML) estimators for the multiple regression coefficients in linear model with the underlying distribution assumed to be symmetric, one of Student's t family. We obtain the closed form of the estimators and derive their asymptotic properties. In addition, we demonstrate that the MML estimators are more appropriate to estimate the parameters in the Capital Asset Pricing Model by comparing its performance with that of least squares estimators (LSE) on the monthly returns of US portfolios. Our empirical study reveals that the MML estimators are more efficient than the LSE in terms of relative efficiency of one-step-ahead forecast mean square error for small samples.Maximum likelihood estimators, Modified maximum likelihood estimators, Student’s t family, Capital Asset Pricing Model, Robustness
Estimating Parameters in Autoregressive Models with Asymmetric Innovations
Tiku et al (1999) considered the estimation in a regression model with autocorrelated error in which the underlying distribution be a shift-scaled Student’s t distribution, developed the modified maximum likelihood (MML) estimators of the parameters and showed that the proposed estimators had closed forms and were remarkably efficient and robust. In this paper, we extend the results to the case, where the underlying distribution is a generalized logistic distribution. The generalized logistic distribution family represents very wide skew distributions ranging from highly right skewed to highly left skewed. Analogously, we develop the MML estimators since the ML (maximum likelihood) estimators are intractable for the generalized logistic data. We then study the asymptotic properties of the proposed estimators and conduct simulation to the study.
Robust Estimation of Multiple Regression Model with Non-normal Error: Symmetric Distribution
In this paper, we develop the modified maximum likelihood (MML) estimators for the multiple regression coefficients in linear model with the underlying distribution assumed to be symmetric, one of Student's t family. We obtain the closed form of the estimators and derive their asymptotic properties. In addition, we demonstrate that the MML estimators are more appropriate to estimate the parameters in the Capital Asset Pricing Model by comparing its performance with that of least squares estimators (LSE) on the monthly returns of US portfolios. Our empirical study reveals that the MML estimators are more efficient than the LSE in terms of relative efficiency of one-step-ahead forecast mean square error for small samples.Maximum likelihood estimators, Modified maximum likelihood estimators, Student's t family, Capital Asset Pricing Model, Robustness.
A Trinomial Test for Paired Data When There are Many Ties
This paper develops a new test, the trinomial test, for pairwise ordinal data samples to improve the power of the sign test by modifying its treatment of zero differences between observations, thereby increasing the use of sample information. Simulations demonstrate the power superiority of the proposed trinomial test statistic over the sign test in small samples in the presence of tie observations. We also show that the proposed trinomial test has substantially higher power than the sign test in large samples and also in the presence of tie observations, as the sign test ignores information from observations resulting in ties.Sign test; trinomial test; non-parametric test; ties; test statistics; hypothesis testing
Robust Estimation and Forecasting of the Capital Asset Pricing Model
In this paper, we develop a modified maximum likelihood (MML) estimator for the multiple linear regression model with underlying student t distribution. We obtain the closed form of the estimators, derive the asymptotic properties, and demonstrate that the MML estimator is more appropriate for estimating the parameters of the Capital Asset Pricing Model by comparing its performance with least squares estimators (LSE) on the monthly returns of US portfolios. The empirical results reveal that the MML estimators are more efficient than LSE in terms of the relative efficiency of one-step-ahead forecast mean square error in small samples.Maximum likelihood estimators, Modified maximum likelihood estimators, Student t family, Capital asset pricing model, Robustness.
Multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution
Multi-frequency and multi-GNSS measurements from modernized satellites are properly integrated for PPP with ambiguity resolution to achieve the state-of-the-art fast and accurate positioning, which provides an important contribution to GNSS precise positioning and applications. The multi-frequency and multi-GNSS PPP phase bias estimation and ambiguity resolution, which is accomplished by a unified model based on the uncombined PPP, are thoroughly evaluated with special focus on Galileo and BDS
THE INFLUENCE OF THE INTEGRATION OF CHINESE TRADITIONAL CULTURE INTO THE TEACHING OF ENVIRONMENTAL DESIGN SPECIALTY ON THE POSITIVE PSYCHOLOGY OF COLLEGE STUDENTS
Robust Estimation and Forecasting of the Capital Asset Pricing Model
In this paper, we develop a modified maximum likelihood (MML) estimator for the multiple linear regression model with underlying student t distribution. We obtain the closed form of the estimators, derive the asymptotic properties, and demonstrate that the MML estimator is more appropriate for estimating the parameters of the Capital Asset Pricing Model by comparing its performance with least squares estimators (LSE) on the monthly returns of US portfolios. The empirical results reveal that the MML estimators are more efficient than LSE in terms of the relative efficiency of one-step-ahead forecast mean square error in small samples.Maximum likelihood estimators; Modified maximum likelihood estimators; Student t family; Capital asset pricing model; Robustness
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