Maximum Likelihood Estimation - AnalyticBridge2019-08-25T18:23:25Zhttps://www.analyticbridge.datasciencecentral.com/forum/topics/2004291:Topic:18757?feed=yes&xn_auth=noIt depends how you define "be…tag:www.analyticbridge.datasciencecentral.com,2008-08-14:2004291:Comment:214082008-08-14T17:37:05.114ZVincent Granvillehttps://www.analyticbridge.datasciencecentral.com/profile/VincentGranville
It depends how you define "best". If you want a robust estimator and your data has some very extreme outliers, than the sample mean is NOT a good estimator of the population mean.
It depends how you define "best". If you want a robust estimator and your data has some very extreme outliers, than the sample mean is NOT a good estimator of the population mean. Hi Surya ,
How are you?
This…tag:www.analyticbridge.datasciencecentral.com,2008-07-24:2004291:Comment:187902008-07-24T15:30:03.754ZRamhttps://www.analyticbridge.datasciencecentral.com/profile/Ram8
Hi Surya ,<br />
How are you?<br />
<br />
This is Ram from Bangalore..
Hi Surya ,<br />
How are you?<br />
<br />
This is Ram from Bangalore.. Thanks.. any proof for this.…tag:www.analyticbridge.datasciencecentral.com,2008-07-24:2004291:Comment:187792008-07-24T11:00:15.640ZSuryahttps://www.analyticbridge.datasciencecentral.com/profile/Surya
Thanks.. any proof for this. Are you saying we can differentiate the binomial function to see which value of p gives me the maximum likelihood?<br />
<br />
i perceive that for models where the parameters are huge in number or when the degree of those parameters increase, we cant conclude in that way.
Thanks.. any proof for this. Are you saying we can differentiate the binomial function to see which value of p gives me the maximum likelihood?<br />
<br />
i perceive that for models where the parameters are huge in number or when the degree of those parameters increase, we cant conclude in that way.