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Tang, Wan; He, Hua; Tu, Xin M's Applied categorical and count data analysis PDF

Posted On April 20, 2018 at 11:50 am by / Comments Off on Tang, Wan; He, Hua; Tu, Xin M's Applied categorical and count data analysis PDF

By Tang, Wan; He, Hua; Tu, Xin M

ISBN-10: 1439806241

ISBN-13: 9781439806241

ISBN-10: 143989793X

ISBN-13: 9781439897935

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Extra info for Applied categorical and count data analysis

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In addition, these packages provide better formatted output, more intuitive user interface, simpler programming, and more detailed documentation. SPSS and Stata even offer a menu-driven system for commonly used procedures so users can point and click on pop-up menus to select the desired models and test statistics. If you are a practitioner interesting in applying statistical models for data analysis, you may choose one of the three commercial packages when working out the examples in the book.

The random variable X n − µ →p 0, where · denotes the Euclidean distance in Rk . 2 Delta Method and Slutsky’s Theorem In many inference problems in applications, we encounter much more complex statistics than sample means. d. sample Xi is s2n = n1 i=1 Xi − X n . Since the terms Xi − X n are not independent, we cannot apply LLN or CLT directly to determine consistency or asymptotic distribution. As will be seen in the subsequent chapters of the book, we often need to determine the consistency and asymptotic distribution of a function of some statistic g θn , where θn is some statistic such as the sample mean and g (·) is some smooth function such as log.

We can write the model as Yi = X i β + i, i ∼ N 0, σ 2 , 1 ≤ i ≤ n. 13) Thus, in regression models, interest lies in the relationship between Yi and X i , while accounting for random variation of Yi given the values of X i and the distribution of X i is of no interest. 13), the log-likelihood function is given by ln (θ) = − n 1 log 2πσ 2 − 2 2 2σ n 2 Yi − X i β . 8): ∂ 1 ln = 2 ∂β σ n X i Yi − X i X i β = 0. 14) 22 Applied Categorical and Count Data Analysis By solving for β, we obtain the MLE β = The second derivative of log f (Yi | X i , θ) is n i=1 −1 X iX i ( 1 ∂2 log f (yi | θ) = − 2 X i X i .

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Applied categorical and count data analysis by Tang, Wan; He, Hua; Tu, Xin M


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