8
Oct 17

Reevaluating probabilities based on piece of evidence

Reevaluating probabilities based on piece of evidence

This actually has to do with the Bayes' theorem. However, in simple problems one can use a dead simple approach: just find probabilities of all elementary events. This post builds upon the post on Significance level and power of test, including the notation. Be sure to review that post.

Here is an example from the guide for Quantitative Finance by A. Patton (University of London course code FN3142).

Activity 7.2 Consider a test that has a Type I error rate of 5%, and power of 50%.

Suppose that, before running the test, the researcher thinks that both the null and the alternative are equally likely.

  1. If the test indicates a rejection of the null hypothesis, what is the probability that the null is false?

  2. If the test indicates a failure to reject the null hypothesis, what is the probability that the null is true?

Denote events R = {Reject null}, A = {fAil to reject null}; T = {null is True}; F = {null is False}. Then we are given:

(1) P(F)=0.5;\ P(T)=0.5;

(2) P(R|T)=\frac{P(R\cap T)}{P(T)}=0.05;\ P(R|F)=\frac{P(R\cap F)}{P(F)}=0.5;

(1) and (2) show that we can find P(R\cap T) and P(R\cap F) and therefore also P(A\cap T) and P(A\cap F). Once we know probabilities of elementary events, we can find everything about everything.

Elementary events

Figure 1. Elementary events

Answering the first question: just plug probabilities in P(F|R)=\frac{P(R\cap F)}{P(R)}=\frac{P(R\cap F)}{P(R\cap T)+P(A\cap T)}.

Answering the second question: just plug probabilities in P(T|A)=\frac{P(A\cap T)}{P(A)}=\frac{P(A\cap T)}{P(A\cap T)+P(A\cap F)}.

Patton uses the Bayes' theorem and the law of total probability. The solution suggested above uses only additivity of probability.

 

6
Oct 17

Significance level and power of test

Significance level and power of test

In this post we discuss several interrelated concepts: null and alternative hypotheses, type I and type II errors and their probabilities. Review the definitions of a sample space and elementary events and that of a conditional probability.

Type I and Type II errors

Regarding the true state of nature we assume two mutually exclusive possibilities: the null hypothesis (like the suspect is guilty) and alternative hypothesis (the suspect is innocent). It's up to us what to call the null and what to call the alternative. However, the statistical procedures are not symmetric: it's easier to measure the probability of rejecting the null when it is true than other involved probabilities. This is why what is desirable to prove is usually designated as the alternative.

Usually in books you can see the following table.

Decision taken
Fail to reject null Reject null
State of nature Null is true Correct decision Type I error
Null is false Type II error Correct decision

This table is not good enough because there is no link to probabilities. The next video does fill in the blanks.

Significance level and power of test

Video. Significance level and power of test

Significance level and power of test

The conclusion from the video is that

\frac{P(T\bigcap R)}{P(T)}=P(R|T)=P\text{(Type I error)=significance level} \frac{P(F\bigcap R)}{P(F)}=P(R|F)=P\text{(Correctly rejecting false null)=Power}
26
Jul 17

Nonlinear least squares: idea, geometry and implementation in Stata

Nonlinear least squares

Here we explain the idea, illustrate the possible problems in Mathematica and, finally, show the implementation in Stata.

Idea: minimize RSS, as in ordinary least squares

Observations come in pairs (x_1,y_1),...,(x_n,y_n). In case of ordinary least squares, we approximated the y's with linear functions of the parameters, possibly nonlinear in x's. Now we use a function f(a,b,x_i) which may be nonlinear in a,b. We still minimize RSS which takes the form RSS=\sum r_i^2=\sum(y_i-f(a,b,x_i))^2. Nonlinear least squares estimators are the values a,b that minimize RSS. In general, it is difficult to find the formula (closed-form solution), so in practice software, such as Stata, is used for RSS minimization.

Simplified idea and problems in one-dimensional case

Suppose we want to minimize f(x). The Newton algorithm (default in Stata) is an iterative procedure that consists of steps:

  1. Select the initial value x_0.
  2. Find the derivative (or tangent) of RSS at x_0. Make a small step in the descent direction (indicated by the derivative), to obtain the next value x_1.
  3. Repeat Step 2, using x_1 as the starting point, until the difference between the values of the objective function at two successive points becomes small. The last point x_n will approximate the minimizing point.

Problems:

  1. The minimizing point may not exist.
  2. When it exists, it may not be unique. In general, there is no way to find out how many local minimums there are and which ones are global.
  3. The minimizing point depends on the initial point.

See Video 1 for illustration in the one-dimensional case.

NLS geometry

Video 1. NLS geometry

Problems illustrated in Mathematica

Here we look at three examples of nonlinear functions, two of which are considered in Dougherty. The first one is a power functions (it can be linearized applying logs) and the second is an exponential function (it cannot be linearized). The third function gives rise to two minimums. The possibilities are illustrated in Mathematica.

NLS illustrated in Mathematica

Video 2. NLS illustrated in Mathematica

 

Finally, implementation in Stata

Here we show how to 1) generate a random vector, 2) create a vector of initial values, and 3) program a nonlinear dependence.

Nonlinear least squares implemented in Stata

Video 3. NLS implemented in Stata

10
Jul 17

Alternatives to simple regression in Stata

Alternatives to simple regression in Stata

In this post we looked at dependence of EARNINGS on S (years of schooling). In the end I suggested to think about possible variations of the model. Specifically, could the dependence be nonlinear? We consider two answers to this question.

Quadratic regression

This name is used for the quadratic dependence of the dependent variable on the independent variable. For our variables the dependence is

EARNINGS=a+bS+cS^2+u.

Note that the dependence on S is quadratic but the right-hand side is linear in the parameters, so we still are in the realm of linear regression. Video 1 shows how to run this regression.

Running quadratic regression in Stata

Video 1. Running quadratic regression in Stata

Nonparametric regression

The general way to write this model is

y=m(x)+u.

The beauty and power of nonparametric regression consists in the fact that we don't need to specify the functional form of dependence of y on x. Therefore there are no parameters to interpret, there is only the fitted curve. There is also the estimated equation of the nonlinear dependence, which is too complex to consider here. I already illustrated the difference between parametric and nonparametric regression. See in Video 2 how to run nonparametric regression in Stata.

Nonparametric dependence

Video 2. Nonparametric dependence

6
Jul 17

Running simple regression in Stata

Running simple regression in Stata is, well, simple. It's just a matter of a couple of clicks. Try to make it a small research.

  1. Obtain descriptive statistics for your data (Statistics > Summaries, tables, and tests > Summary and descriptive statistics > Summary statistics). Look at all that stuff you studied in introductory statistics: units of measurement, means, minimums, maximums, and correlations. Knowing the units of measurement will be important for interpreting regression results; correlations will predict signs of coefficients, etc. In your report, don't just mechanically repeat all those measures; try to find and discuss something interesting.
  2. Visualize your data (Graphics > Twoway graph). On the graph you can observe outliers and discern possible nonlinearity.
  3. After running regression, report the estimated equation. It is called a fitted line and in our case looks like this: Earnings = -13.93+2.45*S (use descriptive names and not abstract X,Y). To see if the coefficient of S is significant, look at its p-value, which is smaller than 0.001. This tells us that at all levels of significance larger than or equal to 0.001 the null that the coefficient of S is significant is rejected. This follows from the definition of p-value. Nobody cares about significance of the intercept. Report also the p-value of the F statistic. It characterizes significance of all nontrivial regressors and is important in case of multiple regression. The last statistic to report is R squared.
  4. Think about possible variations of the model. Could the dependence of Earnings on S be nonlinear? What other determinants of Earnings would you suggest from among the variables in Dougherty's file?
Looking at data

Figure 1. Looking at data. For data, we use a scatterplot.

 

Running regression

Figure 2. Running regression (Statistics > Linear models and related > Linear regression)

29
Jun 17

Introduction to Stata

Introduction to Stata: Stata interface, how to use Stata Help, how to use Data Editor and how to graph data. Important details to remember:

  1. In any program, the first thing to use is Help. I learned everything from Help and never took any programming courses.
  2. The number of observations for all variables in one data file must be the same. This can be a problem if, for example, you want to see out-of-sample predictions.
  3. In Data Editor, numeric variables are displayed in black and strings are displayed in red.
  4. The name of the hidden variable that counts observations is _n
  5. If you have several definitions of graphs in two-way graphs menu, they will be graphed together or separately, depending on what is enabled/disabled.

See details in videos. Sorry about the background noise!

Stata interface

Video 1. Stata interface. The windows introduced: Results, Command, Variables, Properties, Review and Viewer.

Using Stata help

Video 2. Using Stata Help. Help can be used through the Review window or in a separate pdf viewer. Eviews Help is much easier to understand.

Using Data Editor

Video 3. Using Data Editor. How to open and view variables, the visual difference between numeric variables and string variables. The lengths of all variables in the same file must be the same.

Graphing data

Video 4. Graphing data. To graph a variable, you need to define its graph and then display it. It is possible to display more than one variable on the same chart.

21
Feb 17

The pearls of AP Statistics 37

Confidence interval: attach probability or not attach?

I am reading "5 Steps to a 5 AP Statistics, 2010-2011 Edition" by Duane Hinders (sorry, I don't have the latest edition). The tip at the bottom of p.200 says:

For the exam, be VERY, VERY clear on the discussion above. Many students
seem to think that we can attach a probability to our interpretation of a confidence
interval. We cannot.

This is one of those misconceptions that travel from book to book. Below I show how it may have arisen.

Confidence interval derivation

The intuition behind the confidence interval and the confidence interval derivation using z score have been given here. To make the discussion close to Duane Hinders, I show the confidence interval derivation using the t statistic. Let X_1,...,X_n be a sample of independent observations from a normal population, \mu the population mean and s the standard error. Skipping the intuition, let's go directly to the t statistic

(1) t=\frac{\bar{X}-\mu}{s/\sqrt{n}}.

At the 95% confidence level, from statistical tables find the critical value t_{cr,0.95} of the t statistic such that

P(-t_{cr,0.95}<t<t_{cr,0.95})=0.95.

Plug here (1) to get

(2) P(-t_{cr,0.95}<\frac{\bar{X}-\mu}{s/\sqrt{n}}<t_{cr,0.95})=0.95.

Using equivalent transformations of inequalities (multiplying them by s/\sqrt{n} and adding \mu to all sides) we rewrite (2) as

(3) P(\mu-t_{cr,0.95}\frac{s}{\sqrt{n}}<\bar{X}<\mu+t_{cr,0.95}\frac{s}{\sqrt{n}})=0.95.

Thus, we have proved

Statement 1. The interval \mu\pm t_{cr,0.95}\frac{s}{\sqrt{n}} contains the values of the sample mean with probability 95%.

The left-side inequality in (3) is equivalent to \mu<\bar{X}+t_{cr,0.95}\frac{s}{\sqrt{n}} and the right-side one is equivalent to \bar{X}-t_{cr,0.95}\frac{s}{\sqrt{n}}<\mu. Combining these two inequalities, we see that (3) can be equivalently written as

(4) P(\bar{X}-t_{cr,0.95}\frac{s}{\sqrt{n}}<\mu<\bar{X}+t_{cr,0.95}\frac{s}{\sqrt{n}})=0.95.

So, we have

Statement 2. The interval \bar{X}\pm t_{cr,0.95}\frac{s}{\sqrt{n}} contains the population mean with probability 95%.

Source of the misconception

In (3), the variable in the middle (\bar{X}) is random, and the statement that it belongs to some interval is naturally probabilistic. People not familiar with the above derivation don't understand how a statement that the population mean (which is a constant) belongs to some interval can be probabilistic. It's the interval ends that are random in (4) (the sample mean and standard error are both random), that's why there is probability! Statements 1 and 2 are equivalent!

My colleague Aidan Islyami mentioned that we should distinguish estimates from estimators.

In all statistical derivations random variables are ex-ante (before the event). No book says that but that's the way it is. An estimate is an ex-post (after the event) value of an estimator. An estimate is, of course, a number and not a random variable. Ex-ante, a confidence interval always has a probability. Ex-post, the fact that an estimate belongs to some interval is deterministic (has probability either 0 or 1) and it doesn't make sense to talk about 95%.

Since confidence levels are always strictly between 0 and 100%, students should keep in mind that we deal with ex-ante variables.
11
Feb 17

Gauss-Markov theorem

The Gauss-Markov theorem states that the OLS estimator is the most efficient. Without algebra, you cannot make a single step further, whether it is the precise theoretical statement or an application.

Why do we care about linearity?

The concept of linearity has been repeated many times in my posts. Here we have to start from scratch, to apply it to estimators.

The slope in simple regression

(1) y_i=a+bx_i+e_i

can be estimated by

\hat{b}(y,x)=\frac{Cov_u(y,x)}{Var_u(x)}.

Note that the notation makes explicit the dependence of the estimator on x,y. Imagine that we have two sets of observations: (y_1^{(1)},x_1),...,(y_n^{(1)},x_n) and (y_1^{(2)},x_1),...,(y_n^{(2)},x_n) (the x coordinates are the same but the y coordinates are different). In addition, the regressor is deterministic. The x's could be spatial units and the y's temperature measurements at these units at two different moments.

Definition. We say that \hat{b}(y,x) is linear with respect to y if for any two vectors y^{(i)}= (y_1^{(i)},...,y_n^{(i)}), i=1,2, and numbers c,d we have

\hat{b}(cy^{(1)}+dy^{(2)},x)=c\hat{b}(y^{(1)},x)+d\hat{b}(y^{(2)},x).

This definition is quite similar to that of linearity of means. Linearity of the estimator with respect to y easily follows from linearity of covariance

\hat{b}(cy^{(1)}+dy^{(2)},x)=\frac{Cov_u(cy^{(1)}+dy^{(2)},x)}{Var_u(x)}=c\hat{b}(y^{(1)},x)+d\hat{b}(y^{(2)},x).

In addition to knowing how to establish linearity, it's a good idea to be able to see when something is not linear. Recall that linearity implies homogeneity of degree 1. Hence, if something is not homogeneous of degree 1, it cannot be linear. The OLS estimator is not linear in x because it is homogeneous of degree -1 in x:

\hat{b}(y,cx)=\frac{Cov_u(y,cx)}{Var_u(cx)}=\frac{c}{c^2}\frac{Cov_u(y,x)}{Var_u(x)}=\frac{1}{c}\hat{b}(y,x).

Gauss-Markov theorem

Students don't have problems remembering the acronym BLUE: the OLS estimator is Best Linear Unbiased Estimator. Decoding this acronym starts from the end.

  1. An estimator, by definition, is a function of sample data.
  2. Unbiasedness of OLS estimators is thoroughly discussed here.
  3. Linearity of the slope estimator with respect to y has been proved above. Linearity with respect to x is not required.
  4. Now we look at the class of all slope estimators that are linear with respect to y. As an exercise, show that the instrumental variables estimator belongs to this class.

Gauss-Markov Theorem. Under the classical assumptions, the OLS estimator of the slope has the smallest variance in the class of all slope estimators that are linear with respect to y.

In particular, the OLS estimator of the slope is more efficient than the IV estimator. The beauty of this result is that you don't need expressions of their variances (even though they can be derived).

Remark. Even the above formulation is incomplete. In fact, the pair intercept estimator plus slope estimator is efficient. This requires matrix algebra.

 

9
Dec 16

Ditch statistical tables if you have a computer

You don't need statistical tables if you have Excel or Mathematica. Here I give the relevant Excel and Mathematica functions described in Chapter 14 of my book. You can save all the formulas in one spreadsheet or notebook and use it multiple times.

Cumulative Distribution Function of the Standard Normal Distribution

For a given real z, the value of the distribution function of the standard normal is
F(z)=\frac{1}{\sqrt{2\pi }}\int_{-\infty }^{z}\exp (-t^{2}/2)dt.

In Excel, use the formula =NORM.S.DIST(z,TRUE).

In Mathematica, enter CDF[NormalDistribution[0,1],z]

Probability Function of the Binomial Distribution

For given number of successes x, number of trials n and probability p the probability is

P(Binomial=x)=C_{x}^{n}p^{x}(1-p)^{n-x}.

In Excel, use the formula =BINOM.DIST(x,n,p,FALSE)

In Mathematica, enter PDF[BinomialDistribution[n,p],x]

Cumulative Binomial Probabilities

For a given cut-off value x, number of trials n and probability p the cumulative probability is

P(Binomial\leq x)=\sum_{t=0}^{x}C_{t}^{n}p^{t}(1-p)^{n-t}.
In Excel, use the formula =BINOM.DIST(x,n,p,TRUE).

In Mathematica, enter CDF[BinomialDistribution[n,p],x]

Values of the exponential function e^{-\lambda}

In Excel, use the formula =EXP(-lambda)

In Mathematica, enter Exp[-lambda]

Individual Poisson Probabilities

For given number of successes x and arrival rate \lambda the probability is

P(Poisson=x)=\frac{e^{-\lambda }\lambda^{x}}{x!}.
In Excel, use the formula =POISSON.DIST(x,lambda,FALSE)

In Mathematica, enter PDF[PoissonDistribution[lambda],x]

Cumulative Poisson Probabilities

For given cut-off x and arrival rate \lambda the cumulative probability is

P(Poisson\leq x)=\sum_{t=0}^{x}\frac{e^{-\lambda }\lambda ^{t}}{t!}.
In Excel, use the formula =POISSON.DIST(x,lambda,TRUE)

In Mathematica, enter CDF[PoissonDistribution[lambda],x]

Cutoff Points of the Chi-Square Distribution Function

For given probability of the right tail \alpha and degrees of freedom \nu, the cut-off value (critical value) \chi _{\nu,\alpha }^{2} is a solution of the equation
P(\chi _{\nu}^{2}>\chi _{\nu,\alpha }^{2})=\alpha .
In Excel, use the formula =CHISQ.INV.RT(alpha,v)

In Mathematica, enter InverseCDF[ChiSquareDistribution[v],1-alpha]

Cutoff Points for the Student’s t Distribution

For given probability of the right tail \alpha and degrees of freedom \nu, the cut-off value t_{\nu,\alpha } is a solution of the equation P(t_{\nu}>t_{\nu,\alpha })=\alpha.
In Excel, use the formula =T.INV(1-alpha,v)

In Mathematica, enter InverseCDF[StudentTDistribution[v],1-alpha]

Cutoff Points for the F Distribution

For given probability of the right tail \alpha , degrees of freedom v_1 (numerator) and v_2 (denominator), the cut-off value F_{v_1,v_2,\alpha } is a solution of the equation P(F_{v_1,v_2}>F_{v_1,v_2,\alpha })=\alpha.

In Excel, use the formula =F.INV.RT(alpha,v1,v2)

In Mathematica, enter InverseCDF[FRatioDistribution[v1,v2],1-alpha]

26
Nov 16

Properties of correlation

Correlation coefficient: the last block of statistical foundation

Correlation has already been mentioned in

Statistical measures and their geometric roots

Properties of standard deviation

The pearls of AP Statistics 35

Properties of covariance

The pearls of AP Statistics 33

The hierarchy of definitions

Suppose random variables X,Y are not constant. Then their standard deviations are not zero and we can define their correlation as in Chart 1.

correlation-definition

Chart 1. Correlation definition

Properties of correlation

Property 1. Range of the correlation coefficient: for any X,Y one has - 1 \le \rho (X,Y) \le 1.
This follows from the Cauchy-Schwarz inequality, as explained here.

Recall from this post that correlation is cosine of the angle between X-EX and Y-EY.
Property 2. Interpretation of extreme cases. (Part 1) If \rho (X,Y) = 1, then Y = aX + b with a > 0.

(Part 2) If \rho (X,Y) = - 1, then Y = aX + b with a < 0.

Proof. (Part 1) \rho (X,Y) = 1 implies
(1) Cov (X,Y) = \sigma (X)\sigma (Y)
which, in turn, implies that Y is a linear function of X: Y = aX + b (this is the second part of the Cauchy-Schwarz inequality). Further, we can establish the sign of the number a. By the properties of variance and covariance
Cov(X,Y)=Cov(X,aX+b)=aCov(X,X)+Cov(X,b)=aVar(X),

\sigma (Y)=\sigma(aX + b)=\sigma (aX)=|a|\sigma (X).
Plugging this in Eq. (1) we get aVar(X) = |a|\sigma^2(X) and see that a is positive.

The proof of Part 2 is left as an exercise.

Property 3. Suppose we want to measure correlation between weight W and height H of people. The measurements are either in kilos and centimeters {W_k},{H_c} or in pounds and feet {W_p},{H_f}. The correlation coefficient is unit-free in the sense that it does not depend on the units used: \rho (W_k,H_c)=\rho (W_p,H_f). Mathematically speaking, correlation is homogeneous of degree 0 in both arguments.
Proof. One measurement is proportional to another, W_k=aW_p,\ H_c=bH_f with some positive constants a,b. By homogeneity
\rho (W_k,H_c)=\frac{Cov(W_k,H_c)}{\sigma(W_k)\sigma(H_c)}=\frac{Cov(aW_p,bH_f)}{\sigma(aW_p)\sigma(bH_f)}=\frac{abCov(W_p,H_f)}{ab\sigma(W_p)\sigma (H_f)}=\rho (W_p,H_f).