Add to ORKGThis work disusses estimates based on rounded data. The work describes the estimates of parameters in time series AR and MA and in linear regression, the work presents different kinds of estimates based on rounded data. The work focuses on time series model AR(1) and linear regression, where simulations are added to theories and methods are compared on rounded and unrounded data. In adition, the comparison of linear regression is shown at graphs. Powered by TCPDF (www.tcpdf.org
An algorithm for robust fitting of AR models is given, based on a linear regression idea. The new me...
his paper examines the panel data models when the regression coefficients are fixed, random, and mix...
Abstract—Maximum-likelihood (ML) theory presents an ele-gant asymptotic solution for the estimation ...
If random variables don't take discrete values, it is unavoidable that the observed data are of...
Abstract: In this paper presents two methods for determining the degree of differencing required to ...
In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the p...
Observations on continuous populations are often rounded when recorded due to the precision of the r...
<p>Accuracies of estimates (, , and ) and regression coefficients of estimates on their true values ...
It is important that the estimates of the parameters of an autoregressive moving-average (ARMA) mode...
Dempster and Rubin (D&R) in their (1983) JRSSB paper considered the statistical error caused by ...
<p>Estimated RMR time series by means of linear regression, TEE averaging for zero activity, Kalman ...
The thesis deals with maximum likelihood estimators in time series. The reader becomes familiar with...
A number of different kinds of residuals are used in the analysis of generalized linear models. Gene...
1-1. In the analysis of. most time series it is customary to estimate the mean and the trend by fitt...
The most important assumption about time series and econometrics data is stationarity. Therefore, th...
An algorithm for robust fitting of AR models is given, based on a linear regression idea. The new me...
his paper examines the panel data models when the regression coefficients are fixed, random, and mix...
Abstract—Maximum-likelihood (ML) theory presents an ele-gant asymptotic solution for the estimation ...
If random variables don't take discrete values, it is unavoidable that the observed data are of...
Abstract: In this paper presents two methods for determining the degree of differencing required to ...
In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the p...
Observations on continuous populations are often rounded when recorded due to the precision of the r...
<p>Accuracies of estimates (, , and ) and regression coefficients of estimates on their true values ...
It is important that the estimates of the parameters of an autoregressive moving-average (ARMA) mode...
Dempster and Rubin (D&R) in their (1983) JRSSB paper considered the statistical error caused by ...
<p>Estimated RMR time series by means of linear regression, TEE averaging for zero activity, Kalman ...
The thesis deals with maximum likelihood estimators in time series. The reader becomes familiar with...
A number of different kinds of residuals are used in the analysis of generalized linear models. Gene...
1-1. In the analysis of. most time series it is customary to estimate the mean and the trend by fitt...
The most important assumption about time series and econometrics data is stationarity. Therefore, th...
An algorithm for robust fitting of AR models is given, based on a linear regression idea. The new me...
his paper examines the panel data models when the regression coefficients are fixed, random, and mix...
Abstract—Maximum-likelihood (ML) theory presents an ele-gant asymptotic solution for the estimation ...