A Comparison of the Estimators of the Scale Parameter of the Errors Distribution in the L_{1} Regression ()

Carmen D. Saldiva de André^{}, Silvia Nagib Elian^{*}

Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.

**DOI: **10.4236/ojs.2022.122018
PDF
HTML XML
109
Downloads
445
Views
Citations

Institute of Mathematics and Statistics, University of São Paulo, São Paulo, Brazil.

The L_{1} regression is a robust alternative to
the least squares regression whenever there are outliers in the values of the response
variable, or the errors follow a long-tailed distribution. To
calculate the standard errors of the L_{1} estimators, construct confidence
intervals and test hypotheses about the parameters of the model, or to calculate
a robust coefficient of determination, it is necessary to have an estimate of a
scale parameterτ*.* This parameter is such
that τ^{2}/*n* is
the variance of the median of a sample of size *n* from the errors
distribution. [1] proposed
the use of , a consistent, and so, an asymptotically unbiased estimator of τ*.* However, this estimator is not stable in small samples, in the sense that it can
increase with the introduction of new independent variables in the model. When
the errors follow the Laplace distribution, the maximum likelihood estimator of τ*,* say , is the mean absolute error, that is, the mean of the absolute residuals.
This estimator always decreases when new independent variables are added to the
model. Our
objective is to develop asymptotic properties of under several errors distributions analytically.
We also performed a simulation study to compare the distributions of both estimators
in small samples with the objective to establish conditions in which is a good
alternative to for such situations.

Keywords

Minimum Sum of Absolute Errors Regression, Multiple Linear Regression, Variable Selection, Heavy Tail Distributions, Asymptotic Theory

Share and Cite:

de André, C. and Elian, S. (2022) A Comparison of the Estimators of the Scale Parameter of the Errors Distribution in the L_{1} Regression. *Open Journal of Statistics*, **12**, 261-276. doi: 10.4236/ojs.2022.122018.

1. Introduction

Consider the multiple linear regression model

,

where* *

*y* is an *n* × 1 vector of values of the response variable corresponding to *X*, an *n* × *k* matrix of predictor variables that may include a column of ones for the intercept term;* *

*β* is a *k* × 1 vector of unknown parameters and; * *

*ε* is an *n* × 1 vector of unobservable random errors.* *

The components of *ε* are independent and identically distributed random variables with cumulative distribution function *F*. Suppose that *F* has a unique median equal to zero and a continuous derivative *f* in the neighborhood of zero such that *f*(0) > 0. The scale parameter of *f* is defined as * *

. (1.1)* *

So *τ*^{2}/*n* is the variance of the median in a sample of size *n* from the error distribution.* *

The L_{1} estimator of *β*, minimizes for all values of *β*, where *y _{i}* is the

The L_{1} criterion is a robust alternative to the least squares regression whenever the data contains outliers or the errors follow a long tailed distribution such as Laplace or Cauchy.* *

It is well known that when the errors follow Laplace distribution, the L_{1} estimators of *β* are maximum likelihood estimators and so, they are asymptotically unbiased and efficient. [2] proved that the L_{1} estimator is asymptotically unbiased, consistent and follows a multinormal distribution with covariance matrix *τ*^{2}(*X**'**X)*^{−1}. An important implication of this result is that the L_{1} estimator of *β* has a smaller confidence ellipsoid than the least squares estimator for any error distribution for which the sample median is a more efficient estimator than the sample mean.

Based on the asymptotic distribution results, formulae for constructing confidence intervals and testing hypotheses on the parameters of the model have been developed [3] [4]. To apply these formulae and also to compute the standard errors of the estimators of *β,* it is necessary to have an estimate of the parameter *τ*. Several estimators of *τ* were proposed [1]. They recommend the consistent estimator

,

where

,

*n ^{*}* is the number of non-zero residuals and are the non-zero residuals arranged in ascending order.

It is important to observe that is a measure of the variability of the residuals and, although is influenced by all of them, it is determined by only two of them.* *

A consistent estimator of *τ* is also needed to calculate the robust coefficient of determination *R* proposed by [5]. This coefficient is an informal measure of goodness of fit of a model and it is given by* *

,

where

*, *

,

is the predicted value of the response variable in the *i*-th observation, that is;

is the L_{1} estimator of the regression coefficients of the model.

A desirable property for a coefficient of determination is that it increases when passing from a reduced to a full model, that is, when new predictor variables are included in the model [6]. For *R*_{2} this property is true only if decreases as new variables are included in the model and this might not happen, as shown in Example 1.

Example 1—In this example, we use the real state data from [7]. The predictor variables are taxes, in hundred dollars (*X*_{1}), lot area, in thousand squares feet (*X*_{2}), living space, in thousand squares feet (*X*_{3}), age of the home, in years (*X*_{4}). The response variable (*Y*) is the selling price of the home, in thousands of dollars.* *

In Table 1, we present all possible linear models obtained with the four predicted variables, the number of parameters (*k*), the estimates and the values of *R*_{2} for each model. In this table, we see that the value of for the model with variable *X*_{1} only (4.0079) is smaller than the observed value of this statistic in the model with *X*_{1} and *X*_{3} (5.4301), and then the value of *R*_{2} in the model containing only *X*_{1} as predictor is larger than in the model with *X*_{1} and *X*_{3}. However, the contribution of *X*_{3} given that *X*_{1} is already in the model is significant (*p*-value less than 0.01).

So, it may happen that the value of increases even with the introduction of a variable with significant contribution in the model. This fact will decrease the value of *R*_{2} and the new model might not be selected if the coefficient of determination is the criterion to select a model.

This instable behavior of may be explained by the fact that it is determined by only two residuals and so we can expect that it happens more frequently in small samples.

When errors follow the Laplace distribution, the maximum likelihood estimator of *τ* is the mean absolute error [8], given by

where

.

Table 1. Number of parameters (*k*), and *R*_{2} observed values for all possible regression models for the state data.

Although the usual regularity conditions do not hold for Laplace distribution, is a consistent estimator of *τ* [9]. This estimator is a measure of variability of the residuals, and it has the property of decreasing when new predictor variables are included in the model. Using this estimator, it is possible to construct a robust coefficient of determination that satisfies the desirable conditions in [6]. It is possible also to calculate the coefficient of determination adjusted by the number of predictor variables proposed in [10].

Our objective is to study the possibility of using as an alternative to when the errors follow a distribution other than Laplace. We have special interest in small sample sizes because of the instable behavior of in such cases.

[11] pointed out the importance of the L_{1} method of estimation, presenting many practical situations in which its application is recommended. So, the search of procedures that make its use more efficient gives important contribution to the statistical theory.

The paper is organized as follows. Initially, the asymptotic distributions of were derived analytically assuming errors with Normal, Mixture of Normals, Laplace and Logistic distributions. These results allowed to compute the asymptotic bias and mean squared error of this estimator. Then, we performed a simulation study and generated empirical distributions of and in small samples, after the fitting of models with one predictor variable and errors with the same distributions considered previously and Cauchy distribution also. The distributions considered in this study were characterized according to the weight of their tails [12]. The results obtained in this study allowed indicating situations in which can be used as an alternative to estimate *τ.*

2. Asymptotic Distribution of

In this section, we derive analytically the asymptotic distribution of, considering four different distributions for the errors. We assume errors with normal (0, *σ*^{2}) distribution, mixture of Normals when random variables are selected from a normal (0, 1) with probability p and of a normal (0, *σ*^{2}) with probability 1 − *p*, Logistic distribution with mean zero and variance *γ*^{2}π^{2}/3 and Laplace distribution with mean zero and variance 2*σ*^{2}.* *

First, we note that may be written as * *

where* *

is the L_{1} estimator of.* *

Since is a consistent estimator of [2], the asymptotic distribution of is the same of, and this quantity is equal to, where *ε _{i}* is the

3. Errors with Normal (0, *σ*^{2}) Distribution

Using the fact that we observe (see Appendix A) that

and.

Further, the random variables, , are independent and identically distributed and, by the Central-limit theorem

.

So, it follows that

.

Finally, since then, which implies that

,

or that

.

4. Errors with Mixture of Normal Distributions

In this case, we assume that the errors distribution is a mixture of two normal distributions: a Normal (0, 1) selected with probability *p* and a Normal (0, *σ*^{2}) selected with probability (1 − *p*). Hence, the probability density function of *ε _{i}* is

It is not very difficult to see that

, and that the parameter *τ* is

Furthermore, , are independent and identically distributed random variables with mean and variance (see Appendix A) given by

and

.

So, as the same way that in the Normal errors distribution case

,

and

,

that is,

.

5. Errors with Logistic Distribution

Let us suppose that the errors follow a Logistic distribution with probability density function

such that, , and, therefore, *τ* = 2*γ*.

Also, because, , are independent and identically distributed random variables, it is proved in the Appendix A that the mean and the variance of these variables are 1.386*γ* and 1.37*γ*^{2}*. *

So, by the Central-limit theorem

.

Using the same arguments of the previous demonstrations, it follows that

Since in this case *τ* = 2*γ*, we have

.

6. Errors with Laplace Distribution

When the errors follow a Laplace distribution with mean zero and variance 2*σ*^{2} then * *

, and*. *

Therefore, because of the Central-limit theorem* *

,

and hence

.

*Remark*:* *Based on the asymptotic distribution of, confidence intervals for *τ*can be developed. In the Normal errors case, an asymptotic confidence interval for *τ* is

where, , and *z* is the percentile of order (1 + *γ*)/2 of the standard Normal distribution and *γ* is the confidence coefficient of the interval.

This confidence interval enables us to test hypothesis like H: *τ* = *τ*_{0} at a significance level *α* = (1 − *γ*).

7. Asymptotic Bias of

Because the sample mean of the values of the absolute residuals is a continuous and limited function, by the Helly-Bray Lemma [13], the expectation of converges to the mean of its asymptotic distribution. Therefore, it is possible to calculate the asymptotic bias of this estimator.

The analysis of the results presented in the previous section shows that the bias of this estimator is different of zero for every errors distribution considered, except the Laplace distribution.

When the errors follow the Normal (0, *σ*^{2}) distribution, the asymptotic bias is

,

that is negative, and so, in average, sub-estimates *τ*.

For the mixture of Normal distribution errors, the asymptotic bias is

,

that is negative if, and is positive otherwise.

In the Logistic distribution, the asymptotic bias is given by

,

and so, it is always negative.

8. Simulation Study

In this section, we perform a simulation study with the objective of generate empirical distributions of the estimators and considering small sample sizes, under the following distributions of the errors *ε _{i}*:

- Normal (0, 1) (*τ* = 1.253);

- Logistic with mean zero and variance π^{2}/3 (*τ* = 2.00);

- Laplace with mean zero and variance 2 (*τ* = 1.00);

- Mixture of Normals (NM 85-15) when random variables are selected from a Normal (0, 1) with probability 0.85 and a N (0, 49) with probability 0.15 (*τ* = 1.439);

- Mixture of Normals (NM80-20) when random variables are selected from a Normal (0, 1) with probability 0.80 and a N (0, 49) with probability 0.20 (*τ* = 1.513) and

- Cauchy with median zero and scale parameter 1 (*τ* = 1.571).

Our objective is to find situations determined by errors distributions and sample sizes, under which has empirically a better behavior than in terms of bias and mean squared error.* *

The simulation study was designed as follows.

➢ We considered regression models with one independent variable generated from a Normal (0, 1) distribution, independently of the errors. Without loss of generality, the true parameters *β*_{0} and *β*_{1} were fixed equal to 1;

➢ The sample sizes (*n*) were set as 10, 20, 30, 50, 100 and 200;

➢ For each combination of sample size and errors distribution, 1000 sets of data were generated;

➢ Using the L_{1} method, a regression model was fitted for each set of data and the values of and were calculated. So, this procedure generated 1000 values of and.

The computations were performed using a special routine constructed in S-Plus 4.5.

The results obtained in this study are summarized in Tables B1-B6 in Appendix B. They suggest that

➢ is a good alternative to when the errors follow Laplace, NM 85-15 or NM 80-20 distributions. In these cases, has bias and mean squared error smaller or of the same order than;

➢ When the errors follow Normal or Logistic distribution, tends to sub-estimate *τ*. The means of distributions generated in the study are closer to the parameter value and its mean squared errors are in general uniformly smaller than that of, for all considered sample sizes.

➢ For the Cauchy distribution errors, tends to super-estimate *τ.* This result may be a consequence of the fact that all the residuals are considered in the computation of this estimator. Although has smaller bias and mean squared error than, does not seem to be a good estimator of *τ* for sample sizes smaller or equal to 30.

9. Some Characteristics of the Distributions in the Study

The distributions considered in the previous sections are symmetrical about zero and can be ordered by the weight of their tails [12]. For, an appropriate coefficient that can be used with this objective is

*, *

where

,

*F* (*x*) is the distribution function of the errors and

is the median of the density function associated to.

This coefficient has the following properties

Table 2. Values of *b*_{2}(*α*) for the distributions in the study.

➢ ;

➢ Its computation does not require that the errors distribution have any finite moment;

➢ Its value is independent of the parameters of location and scale.

Large values of *b*_{2}(*α*) indicate that the distribution has heavy tails.* *

In Table 2 we present the values of *b*_{2}(*α*) for the distributions considered in this study, taking *α* = 0.10 and *α* = 0.05. For these values of *α*, it is clear that the ordering of the distribution according to its tails weights is: Normal, Logistic, Laplace, NM 85-15, NM 80-20 and Cauchy.

10. Concluding Remarks

In this paper, we studied the behavior of the estimator with the objective of using it as an alternative to. We also determined analytically its asymptotic distribution under different distributions of the errors of the model. It was observed that, in general, is asymptotically biased, with asymptotic bias equal to zero when the errors follow the Laplace distribution. In this case, the absence of asymptotic bias was already expected, since is the maximum likelihood estimator of *τ* when the errors follow the Laplace distribution.* *

Performing a simulation study, the two estimators were compared empirically by their bias and mean squared error, under distributions with different tails weights and considering sample sizes varying from 10 to 200. The results suggest that is a good alternative to when the errors in the model follow Laplace or Mixture of Normal distributions with the values of the parameters fixed in the study; when the errors have Normal or Logistic distributions (lighter tails) or Cauchy distribution (heavy tails), presented the best performance for every considered sample sizes. However, in the Cauchy distribution case, although seemed to be better than, its use is not recommended in samples of size smaller or equal to 30 because of the bias of this estimator.* *

The results of the study indicate that should be used when the distribution of the errors is close to the Laplace distribution, whatever the sample size. By the properties of this estimator mentioned in Section 1, we suggest that the fit of the data to the Laplace distribution be analyzed by the construction of a Q-Q plot of the residuals of the model. If there are not serious deviations, should be used. Otherwise, Box-Cox transformations can be applied following [14]. After this, in the analysis of the transformed data, can be used to construct confidence intervals and hypotheses tests about the parameters of the model and in the computation of robust coefficients of determination with and without a correction by the number of independent variables in the model.* *

Appendix A. Results Used in Section 2

In Section 2, when we obtained the asymptotic distributions of, the distributions of the errors were symmetrical about zero. It is easy to see that if *X* is a random variable with values in the interval ]−∞, ∞[, symmetric about zero and with density *f*(*x*), then the density of is

Using this fact, we got *E*(|*ε _{i}*|) for

If the errors follow a Normal distribution, that is, , then has the density

and thus

Also, , and so *.*

When the errors follow a mixture of Normal distribution, the probability density function of *U _{i}* = |

Therefore* *

and

^{ }

that is the variance of a random variable with mixture of Normal distributions with parameters *p,* (1 − *p*), means equal to zero and variances 1 and *σ*^{2} respectively.

Consequently,

When *ε _{i}* has Logistic distribution with mean zero and variance

because.

Also, and so .

For *ε*_{i} with Laplace distribution with zero mean and variance 2*σ*^{2}, |*ε*_{i}| has exponential distribution with mean equal to *σ* and variance equal to *σ*^{2}. Thus, and. * *

Appendix B. Tables

Table B1. Values of descriptive statistics observed in the distributions of the estimators and generated in the simulation study for models with Normal (0, 1) errors (*τ* = 1.253) and different sample sizes.

Table B2. Values of descriptive statistics observed in the distributions of the estimators and generated in the simulation study for models with Logistic errors (*τ* = 2.00) and different sample sizes.

Table B3. Values of descriptive statistics observed in the distributions of the estimators and generated in the simulation study for models with Laplace errors (*τ* = 1.00) and different sample sizes.

Table B4. Values of descriptive statistics observed in the distributions of the estimators and generated in the simulation study for models with 0.85 Normal (0, 1) + 0.15 Normal (0, 49) errors (*τ* = 1.439) and different sample sizes.

Table B5. Values of descriptive statistics observed in the distributions of the estimators and generated in the simulation study for models with 0.80 Normal (0, 1) + 0.20 Normal (0, 49) errors (*τ* = 1.513) and different sample sizes.

Table B6. Values of descriptive statistics observed in the distributions of the estimators and generated in the simulation study for models with Cauchy errors (*τ* = 1.571) and different sample sizes.

Conflicts of Interest

The authors declare no conflicts of interest.

[1] | McKean, J.W. and Schrader, R.M. (1987) Least Absolute Errors Analysis of Variance. In: Dodge, Y., Eds., Statistical Data Analysis Based on the L1-Norm and Related Methods, Elsevier Science Publishers B. V., Amsterdam, 297-305. |

[2] |
Basset, G. and Koenker, R. (1978) Asymptotic Theory of Least Absolute Error Regression. Journal of the American Statistical Association, 73, 618-622.
https://doi.org/10.1080/01621459.1978.10480065 |

[3] | Dielman, T. and Pfaffenberger, R. (1982) LAV (Least Absolute Value) Estimation in the Regression Model: A Review. TIMS Studies in the Management Sciences, 19, 31-52. |

[4] |
Narula, S.C. (1987) The Minimum Sum of Absolute Errors Regression. Journal of Quality Technology, 19, 37-45. https://doi.org/10.1080/00224065.1987.11979031 |

[5] |
McKean, J.W. and Sievers, G.L. (1987) Coefficient of Determination for Least Absolute Deviation Analysis. Statistics and Probability Letters, 5, 49-54.
https://doi.org/10.1016/0167-7152(87)90026-5 |

[6] |
Kvalseth, T.O. (1985) Cautionary Note about R2. American Statistician, 39, 279-285.
https://doi.org/10.1080/00031305.1985.10479448 |

[7] |
Narula, S.C. and Wellington, J.F. (1977) Prediction, Linear Regression and Minimum Sum of Absolute Errors. Technometrics, 19, 185-190.
https://doi.org/10.1080/00401706.1977.10489526 |

[8] |
Elian, S.N., André, C.D.S. and Narula, S.C. (2000) Influence Measure for the L1 Regression. Communication in Statistics—Theory and Methods, 29, 837-849.
https://doi.org/10.1080/03610920008832518 |

[9] |
Engelhardt, M. and Bain, L.J. (1973) Interval Estimation for the Two-Parameter Double Exponential Ditribution. Technometrics, 15, 875-887.
https://doi.org/10.1080/00401706.1973.10489120 |

[10] |
André, C.D.S., Elian, S.N., Narula, S.C. and Tavares, R.A. (2000) Coefficients of Determination for Variable Selection in the MSAE Regression. Communication in Statistics—Theory and Methods, 29, 623-642.
https://doi.org/10.1080/03610920008832506 |

[11] |
Portnoy, S. and Koenker, R. (1997) The Gaussian Hare and the Laplacian Tortoise: Computability of Squared-Error versus Absolute-Error Estimators. Statistical Science, 12, 279-300. https://doi.org/10.1214/ss/1030037960 |

[12] |
Groeneveld, R.A. and Meeden, G. (1984) Measuring Skewness and Kurtosis. The Statistician, 33, 391-399. https://doi.org/10.2307/2987742 |

[13] | Sen, P.K. and Singer, J.M. (1993) Large Sample Methods Is Statistics—An Introduction with Applications. Chapman and Hall, New York. |

[14] |
Parker, I. (1988) Transformations and Influential Observations in Minimum Sum of Absolute Errors Regression. Technometrics, 30, 215-220.
https://doi.org/10.1080/00401706.1988.10488369 |

Journals Menu

Contact us

+1 323-425-8868 | |

customer@scirp.org | |

+86 18163351462(WhatsApp) | |

1655362766 | |

Paper Publishing WeChat |

Copyright © 2024 by authors and Scientific Research Publishing Inc.

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.