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Poisson    音标拼音: [p'ɔɪzsən]
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  • Why is Poisson regression used for count data?
    Poisson distributed data is intrinsically integer-valued, which makes sense for count data Ordinary Least Squares (OLS, which you call "linear regression") assumes that true values are normally distributed around the expected value and can take any real value, positive or negative, integer or fractional, whatever
  • Poisson or quasi poisson in a regression with count data and . . .
    So now, I'm trying a regression with Poisson Errors With a model with all significant variables, I get: Null deviance: 12593 2 on 53 degrees of freedom Residual deviance: 1161 3 on 37 degrees of freedom AIC: 1573 7 Number of Fisher Scoring iterations: 5 Residual deviance is larger than residual degrees of freedom: I have overdispersion
  • Relationship between poisson and exponential distribution
    Exponential pdf can be used to model waiting times between any two successive poisson hits while poisson models the probability of number of hits Poisson is discrete while exponential is continuous distribution It would be interesting to see a real life example where the two come into play at the same time $\endgroup$ –
  • Poisson regression to estimate relative risk for binary outcomes
    The same cannot be said of relative risk or Poisson models A poisson model is useful too when individuals may have an "outcome" more than once, and you might be interested in cumulative incidence, such as outbreaks of herpes, hospitalizations, or breast cancers For this reason, exponentiated coefficients can be interpreted as relative rates
  • probability - Approximation of binomial distribution - Poisson vs . . .
    Stack Exchange Network Stack Exchange network consists of 183 Q A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers
  • When to use Binomial Distribution vs. Poisson Distribution?
    For Poisson, bike might get fixed and break yet again in the same day (as said by @JMoravitz) Still, if the chosen time interval (day is an arbitrary choice) is narrowed down to so small that likelihood of breaking twice becomes negligible, Binomial is the model for the distribution
  • spss - Difference between binomial, negative binomial and Poisson . . .
    Poisson or NB regression gives the more appropriate effect measure (IRR) versus odds ratio from logistic regression NB regression is "safer" to run than Poisson regression because even if the overdispersion parameter (alpha in Stata) is not statistically significant, the results will be exactly the same as its Poisson regression form
  • dispersion parameter in Poisson models - Cross Validated
    For the Poisson family, the bias looks like this with varying mean The blue is the deviance-based estimate; the orange is the estimate based on Pearson residuals This is a related phenomenon to $\chi^2$ tests in contingency tables not being very accurate with small cell counts -- again, the $\chi^2$ approximation is based on Poisson
  • How can I test if given samples are taken from a Poisson distribution?
    First of all my advice is you must refrain from trying out a Poisson distribution just as it is to the data I suggest you must first make a theory as to why should Poisson distribution fit a particular dataset or a phenomenon Once you have established this, the next question is whether the distribution is homogeneous or not
  • generalized linear model - log transforming a Poisson variable - Cross . . .
    As comments and their links note, there's a difference between a Poisson GLM with a log link and ordinary least squares (OLS) linear regression with a log-transformed count outcome The Poisson GLM models the log of the expected value of the counts, based on the equality of mean and variance for a Poisson-distributed variable





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