Akaike Information Criteria: A Comprehensive Guide

The Akaike Information Criteria/Criterion (AIC) is a method used in statistics and machine learning to compare the relative quality of different models for a given dataset. The AIC method helps in selecting the best model out of a bunch by penalizing models that are overly complex. Akaike Information Criterion provides a means for comparing among models i.e. a tool for model selection.

  • A too-simple model leads to a large approximation error.
  • A too-complex model leads to a large estimation error.

AIC is a measure of goodness of fit of a statistical model developed by Hirotsugo Akaike under the name of “an information Criteria (AIC) and published by him in 1974 first time. It is grounded in the concept of information entropy in between bias and variance in model construction or between accuracy and complexity of the model.

The Formula of Akaike Information Criteria

Given a data set, several candidate models can be ranked according to their AIC values. From AIC values one may infer that the top two models are roughly in a tie and the rest far worse.

$$AIC = 2k-ln(L)$$

where $k$ is the number of parameters in the model, and $L$ is the maximized value of the likelihood function for the estimated model.

Akaike Information Criteria/ Criterion (AIC)

For a set of candidate models for the data, the preferred model is the one that has a minimum AIC value. AIC estimates relative support for a model, which means that AIC scores by themselves are not very meaningful

Akaike Information Criteria focuses on:

  • Balances fit and complexity: A model that perfectly fits the data might not be the best because it might be memorizing the data instead of capturing the underlying trend. AIC considers both how well a model fits the data (goodness of fit) and how complex it is (number of variables).
  • A lower score is better: Models having lower AIC scores are preferred as they achieve a good balance between fitting the data and avoiding overfitting.
  • Comparison tool: AIC scores are most meaningful when comparing models for the same dataset. The model with the lowest AIC score is considered the best relative to the other models being evaluated.

Summary

The AIC score is a single number and is used as model selection criteria. One cannot interpret the AIC score in isolation. However, one can compare the AIC scores of different model fits to the same data. The model with the lowest AIC is generally considered the best choice.

The AIC is the most useful model selection criterion when there are multiple candidate models to choose from. It works well for larger datasets. However, for smaller datasets, the corrected AIC should be preferred. AIC is not perfect, and there can be situations where it fails to choose the optimal model.

There are many other model selection criteria. For more detail read the article: Model Selection Criteria

Akaike Information Criteria

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Estimation of Population Parameters

Introduction to Estimation of Population Parameters

In statistics, estimating population parameters is important because it allows the researcher to conclude a population (whole group) by analyzing a small part of that population. The estimation of population parameters is done when the population under study is large enough. For example, instead of performing a census, a random sample from the population can be drawn. To draw some conclusions about the population, one can calculate the required sample statistic(s).

Important Terminologies

The following are some important terminologies to understand the concept of estimating the population parameters.

  • Population: The entire collection of individuals or items one is interested in studying. For instance, all the people living in a particular country.
  • Sample: A subgroup (or small portion) chosen from the population that represents the larger group.
  • Parameter: A characteristic that describes the entire population, such as the population mean, median, or standard deviation.
  • Statistic: A value calculated from the sample data used to estimate the population parameter. For example, the sample mean is an estimate of the population mean. It is the characteristics of the sample under study.

Various statistical methods are used to estimate population parameters with different levels of accuracy. The accuracy of the estimate depends on the size of the sample and how well the sample represents the population.

We use statistics calculated from the sample data as estimates for the population parameters.

Estimation of Population Parameters Sample Statistic, Population Parameter
  • Sample mean: is used to estimate the population mean. It is calculated by averaging the values of all observations in the sample, that is the sum of all data values divided by the total number of observations in the data.
  • Sample proportion: is used to estimate the population proportion (percentage). It represents the number of successes (events of interest) divided by the total sample size.
  • Sample standard deviation: is used to estimate the population standard deviation. It reflects how spread out the data points are in the sample.

Types of Estimates

There are two types of estimates:

Estimation of Population Parameters: Point Estimate and Interval Estimate
  • Point Estimate: A single value used to estimate the population parameter. The example of point estimates are:
    • The mean/average height of Boys in Colleges is 65 inches.
    • 65% of Lahore residents support a ban on cell phone use while driving.
  • Interval Estimate: It is a set of values (interval) that is supposed to contain the population parameter. Examples of interval estimates are:
    • The mean height of Boys in Colleges lies between 63.5 and 66.5 inches.
    • 65% ($\pm 3$% of Lahore residents support a ban on cell phone use during driving.

Some Examples

Estimation of population parameters is widely used in various fields of life. For example,

  • a company might estimate customer satisfaction through a sample survey,
  • a biologist might estimate the average wingspan of a specific bird species by capturing and measuring a small group.

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Empirical Probability Examples

Introduction to Empirical Probability

An empirical probability (also called experimental probability) is calculated by collecting data from past trials of the experiments. The experimental probability obtained is used to predict the future likelihood of the event occurring.

Formula and Examples Empirical/ Experimental Probability

To calculate an empirical/ experimental probability, one can use the formula

$$P(A)=\frac{\text{Number of trials in which $A$ occurs} }{$\text{Total number of trials}}$$

  • Coin Flip: Let us flip a coin 200 times and get heads 105 times. The empirical probability of getting heads is $\frac{105}{200} = 0.525%, or 52.5%.
  • Weather Prediction: Let you track the weather for a month and see that it rained 12 out of 30 days. The empirical probability of rain on a given day that month is $\frac{12}{30} = 0.4$ or 40%.
  • Plant Growth: Let you plant 50 seeds and 35 sprout into seedlings. The experimental probability of a seed sprouting is $\frac{35}{50} = 0.70$ or 70%.
  • Board Game: Suppose you play a new board game 10 times and win 6 times. The empirical probability of winning the game is $\frac{6}{10} = 0.6$ or 60%.
  • Customer Preferences: In a survey of 100 customers, 80 prefer chocolate chip cookies over oatmeal raisins. The empirical probability of a customer preferring chocolate chip cookies is $\frac{80}{100} = 0.80$ or 80%.
  • Basketball Game: A basketball player practices free throws and makes 18 out of 25 attempts. The experimental probability of the player making their next free throw is $\frac{18}{25} = 0.72$ or 72%.

Empirical Probability From Frequency Tables

A frequency table calculates the probability that a certain data value falls into any data group or class. Consider the frequency table of examination scores in a certain class.

ClassFrequency ($f$)$frf$
40 – 491$\frac{1}{20}=0.05$
50 – 592$\frac{1}{20}=0.10$
60 – 693$\frac{3}{20}=0.15$
70 – 794$\frac{4}{20}=0.20$
80 – 896$\frac{6}{20}=0.30$
90 – 994$\frac{4}{20}=0.20$

Let event $A$ be the event that a student scores between 90 and 99 on the exam, then

$$P(A) = \frac{\text{Number of students scoring 90-99}}{\text{Total number of students}} = \frac{4}{20} = 0.20$$

Notice that $P(A)$ is the relative frequency of the class 90-99.

Empirical Probability and Classical Probability

Key Points Empirical/ Experimental Probability

  • It is based on actual data, not theoretical models.
  • It is a good approach when the data is from similar events in the past.
  • The more data you have, the more accurate the estimate will be.
  • It is not always perfect, as past results do not guarantee future outcomes.

Limitations Empirical/ Experimental Probability

  • It can be time-consuming and expensive to collect enough data.
  • It may not be representative of the future, especially if the underlying conditions change.

FAQS about Empirical/ Experimental Probability

  1. Define empirical probability.
  2. How one can compute empirical probability, write the formula of empirical probability.
  3. Give real-life examples of empirical/ experimental probability.
  4. What are the limitations of empirical/ experimental probability?
  5. How does empirical/ experimental probability resemble with frequency distribution, explain.
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