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## Intro

Maximum likelihood estimation is a very useful technique to fit a model to data used a lot in

econometrics and other sciences, but seems, at least to my knowledge, to not be so well known by

machine learning practitioners (but I may be wrong about that). Other useful techniques to confront models to data

used in econometrics are the minimum distance family of techniques such as the general method of

moments or Bayesian approaches, while machine learning practitioners seem to favor the minimization

of a loss function (the mean squared error in the case of linear regression for instance).

When I taught at the university, students had often some problems to understand the technique.

It is true that it is not as easy to understand as ordinary least squares, but I’ll try to explain

to the best of my abilities.

Given a sample of data, what is the unknown probability distribution

that *most likely* generated it? For instance, if your sample only contains 0’s and 1’s, and

the proportion of 1’s is 80%, what do you think is the most likely distribution that generated it?

The probability distribution that *most likely* generated such a dataset is a binomial distribution

with probability of success equal to 80%. It *might have been* a binomial distribution with probability

of success equal to, say, 60%, but the *most likely* one is one with probability of success equal

to 80%.

To perform maximum likelihood estimation, one thus needs to assume a certain probability distribution,

and then look for the parameters that maximize the likelihood that this distribution generated the

observed data. So, now the question is, how to maximize this likelihood? And mathematically speaking,

what is a *likelihood*?

## Some theory

First of all, let’s assume that each observation from your dataset not only was generated from the

same distribution, but that each observation is also independent from each other. For instance, if in your sample

you have data on people’s wages and socio-economic background, it is safe to assume, under certain

circumstances, that the observations are independent.

Let \(X_i\) be random variables, and \(x_i\) be their realizations (actual observed values).

Let’s assume that the \(X_i\) are distributed according

to a certain probability distribution \(D\) with density \(f(\theta)\) where \(\theta\) is

a parameter of said distribution.

Because our sample is composed of i.i.d. random variables, the probability that it was generated by

our distribution \(D(\theta)\) is:

\[\prod_{i=1}^N Pr(X_i = x_i)\]

It is customary to take the log of this expression:

\[\log(\prod_{i=1}^N Pr(X_i = x_i)) = \sum_{i=1}^N \log(Pr(X_i = x_i))\]

The expression above is called the *log-likelihood*, \(logL(\theta; x_1, …, x_N)\). Maximizing this

function yields \(\theta^*\), the value of the parameter that makes the sample the most probable.

In the case of linear regression, the density function to use is the one from the Normal distribution.

## Maximum likelihood of the linear model as an example of the split-apply-combine strategy

Hadley Wickham’s seminal paper, The Split-Apply-Combine Strategy for Data Analysis

presents the *split-apply-combine* strategy, which should remind the reader of the map-reduce

framework from Google. The idea is to recognize that in some cases big problems are simply an

aggregation of smaller problems. This is the case for Maximum Likelihood Estimation of the linear

model as well.

The picture below illustrates how Maximum Likelihood works, in the standard case:

Let’s use R to do exactly this. Let’s first start by simulating some data:

```
library("tidyverse")
size <- 500000
x1 <- rnorm(size)
x2 <- rnorm(size)
x3 <- rnorm(size)
dep_y <- 1.5 + 2*x1 + 3*x2 + 4*x3 + rnorm(size)
x_data <- cbind(dep_y, 1, x1, x2, x3)
x_df <- as.data.frame(x_data) %>%
rename(iota = V2)
head(x_df)
```

```
## dep_y iota x1 x2 x3
## 1 1.637044 1 0.2287198 0.91609653 -0.4006215
## 2 -1.684578 1 1.2780291 -0.02468559 -1.4020914
## 3 1.289595 1 1.0524842 0.30206515 -0.3553641
## 4 -3.769575 1 -2.5763576 0.13864796 -0.3181661
## 5 13.110239 1 -0.9376462 0.77965301 3.0351646
## 6 5.059152 1 0.7488792 -0.10049061 0.1307225
```

Now that this is done, let’s write a function to perform Maximum Likelihood Estimation:

```
loglik_linmod <- function(parameters, x_data){
sum_log_likelihood <- x_data %>%
mutate(log_likelihood =
dnorm(dep_y,
mean = iota*parameters[1] + x1*parameters[2] + x2*parameters[3] + x3*parameters[4],
sd = parameters[5],
log = TRUE)) %>%
summarise(sum(log_likelihood))
-1 * sum_log_likelihood
}
```

The function returns minus the log likelihood, because `optim()`

which I will be using to optimize

the log-likelihood function minimizes functions by default (minimizing the opposite of a function is the

same as maximizing a function). Let’s optimize the function and see if we’re able to find the

parameters of the data generating process, `1.5, 2, 3, 4`

and `1`

(the standard deviation of the

error term):

`optim(c(1,1,1,1,1), loglik_linmod, x_data = x_df)`

We successfully find the parameters of our data generating process!

Now, what if I’d like to distribute the computation of the contribution to the likelihood of each

observations across my 12 cores? The goal is not necessarily to speed up the computations but

to be able to handle larger than RAM data. If I have data that is too large to fit in memory,

I could split it into chunks, compute the contributions to the likelihood of each chunk, sum

everything again, and voila! This is illustrated below:

To do this, I use the `{disk.frame}`

package, and only need to change my `loglik_linmod()`

function

slightly:

```
library("disk.frame")
x_diskframe <- as.disk.frame(x_df) #Convert the data frame to a disk.frame
loglik_linmod_df <- function(parameters, x_data){
sum_log_likelihood <- x_data %>%
mutate(log_likelihood =
dnorm(dep_y,
mean = iota*parameters[1] + x1*parameters[2] + x2*parameters[3] + x3*parameters[4],
sd = parameters[5],
log = TRUE)) %>%
chunk_summarise(sum(log_likelihood))
out <- sum_log_likelihood %>%
collect() %>%
pull() %>%
sum()
-out
}
```

The function is applied to each chunk, and `chunk_summarise()`

computes the sum of the contributions

inside each chunk. Thus, I first need to use `collect()`

to transfer the chunk-wise sums in memory

and then use `pull()`

to convert it to an atomic vector, and finally sum them all again.

Let’s now optimize this function:

`optim(rep(1, 5), loglik_linmod_df, x_data = x_diskframe)`

```
## $par
## [1] 1.5351722 1.9566144 3.0067978 4.0202956 0.9889412
##
## $value
## [1] 709977.2
##
## $counts
## function gradient
## 502 NA
##
## $convergence
## [1] 1
##
## $message
## NULL
```

This is how you can use the split-apply-combine approach for maximum likelihood estimation of a

linear model! This approach is quite powerful, and the familiar `map()`

and `reduce()`

functions

included in `{purrr}`

can also help with this task. However, this only works if you can split your

problem into chunks, which is sometimes quite hard to achieve.

However, as usual, there is rarely a need to write your own functions, as `{disk.frame}`

includes

the `dfglm()`

function which can be used to estimate any generalized linear model using `disk.frame`

objects!

Hope you enjoyed! If you found this blog post useful, you might want to follow

me on twitter for blog post updates and

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