I was learning from Elements of statistics p.120 under section 4.4.1 Fitting Logistics Regression Models

The log likelihood function was given as

$l(\beta) = \sum_{i=1}^N {y_i\log p(x_i;\beta) + (1-y_i)log(1-p(x_i;\beta))}$


$$\beta = {\{\beta_{10},\beta_1}\} \qquad (1)$$

and we assume that the vector of inputs $x_i$ includes the constant term 1 to accommodate the intercept.

Please my question is :

Assuming we have only two inputs $X$ = $X_1$ + $X_2$ and adding the intercept or constant term ($X_0)$ that contains only 1's, we will have $X$ = $X_1 + X_2 + X_0$. When we find $\beta$ using linear regression, it will be a vector in $R^3$ or the vector will contain three elements i.e $\beta = \{ b_1,b_2,b_3 \}$

How did they get $\beta_{10}$ in $(1)$ and also I want to know if $\beta_{10}$ and $\beta_1$ in $(1)$ are scalars or their vectors


1 Answer 1


To address your first question, assuming that you have two features, $X_1$ and $X_2$, and you choose to include an intercept, the number of parameters that you would expect to estimate is 3, i.e. $\beta = \{\beta_0, \beta_1, \beta_2\}$. However, if you define a new variable $X = X_{1}+X_{2}$, we could treat this as a single feature and therefore model using $\beta = \{\beta_0, \beta_1\}$

To address your second question regarding $\beta_{10}$ and $\beta_1$. If you look at the next statement on page 120

\begin{equation} l(\beta) = \sum_{i=1}^N {y_i\log p(x_i;\beta) + (1-y_i)log(1-p(x_i;\beta))} \\ = \sum_{i=1}^N {y_i\beta^Tx_i - log(1+e^{\beta^Tx_i})} \end{equation}

We see that $\beta$ is treated as vector which suggest that $\beta_{10}$ and $\beta_{1}$ are the components of $\beta$. Based on this, I would assume that the authors are implying these are scalar values.

  • $\begingroup$ Please meaning you can't use th logistics regression when you have 8 or less features. This is because we can't get $ \beta_{10} $ $\endgroup$
    – EA Lehn
    Nov 1, 2020 at 9:48
  • 1
    $\begingroup$ You can have more than 8 features - the 10 does not mean Ten. If you have a look on page 119, the authors state that a model with K classes is specified in terms of $K-1$ logit odds - each with their own set of parameters. In your example $K=2$, meaning their is only 1 set. The 1 in $\beta_{10}$ corresponds to this first and only set, while the 0 corresponds to the intercept. $\endgroup$
    – nwaldo
    Nov 1, 2020 at 15:55
  • $\begingroup$ please can you help me with this question stats.stackexchange.com/questions/494532/parameters-of-lda $\endgroup$
    – EA Lehn
    Nov 1, 2020 at 23:08

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