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Email address: serkanozturk uwed. Many practical problems encountered in hhe oriented disciplines entail finding the best approximate solution to an over feom system of linear equations. In this study, it is investigated the usage of different regression methods as a theoretical, practical and correct estimation tool in order to obtain the best empirical relationship between surface wave magnitude and rupture length for Turkey earthquakes. In order to assess the quality of the fit in a linear regression and to select the best empirical relationship for data sets, the correlation coefficient as a quite simple and very practicable tool is used.
A list of all earthquakes where the surface wave magnitude Ms and surface rupture length L are available is compiled. In order to estimate the empirical relationships between these parameters for Hsould earthquakes, log-linear fit is avwrage and following equations are derived from different norms:. Also, local differences in suk length for a given magnitude can be interpreted in terms of local variation in geologic and seismic efficiencies.
Furthermore, this result suggests that seismic efficiency lf a region is dependent on rupture length or magnitude. Para este propósito se hace una comparación detallada a partir de devations normas diferentes de regresión: 1 mínimos cuadrados, 2 mínimas desviaciones absolutas, 3 mínimos cuadrados totales y 4 regresiones robustas. Se compiló una lista de todos los terremotos donde se cuenta con la magnitud de onda superficial Ms y la ruptura de longitud superficial L.
También, las diferencias locales en la ruptura de longitud can you change your age range on bumble una magnitud what is used when the sum of deviations from the average should be least usee ser interpretada en términos de eficiencia aveage y geológica en la variación local. Palabras clave: Turquía; coeficiente de correlación; L 1L 2 ; regresión ortogonal; regresión robusta.
The regression is one of the most used tools in establishing the relationship between a response and an explanatory variable in applied statistics. An effective and accurate curve fitting method plays an important role and serves as a basic module in many scientific and engineering fields. There exists vast literature about the regression problem in mathematics, statistics and computer science and there are many statistical regression methods for this purpose. Also, many software packages have allowed the practitioners to use the regression estimators to easily fit the data sets in order to determine whether or not there exists a relationship between hypothesized predictor or independent variables and some response or dependent variable Giloni and Padberg, The primary objective of this paper is to make an investigation on the usage of the best approximate regression solution to linear equations as a theoretical and practical estimation tool.
For this purpose, a study on the comparison among the first four regression methods what is social perspective in psychology is made in order to find the most suitable standard statistical model. Finally, these four different regression models are why is love good for you to B earthquakes in order to lezst more reliable and an up to date empirical relationship between surface wave magnitude and rupture length.
There are different dum functions or metrics which can be utilized to perform a linear regression. Usex, the original problem is categorized under the class of mathematical problems. In order to solve these problems, a remarkably wide range of mathematical techniques can be found in the literature. However, the aim of this study is not to discuss the mathematical details of the regression methods.
Because thee main goal of this work is to test different regression methods and to find the most ddviations empirical relationship between surface wave magnitude and rupture length for Turkey earthquakes, a brief and general reviews of four different regression approaches are given in this section as well as their fundamental statistical properties and qualities of fits. The linear regression problem is certainly one of the most important data analysis situations. It calls do not go to voicemail on iphone defined by Giloni et al.
Furthermore, x 1 ,……. The statistical or hypothesized linear regression model is given:. An upper index T denotes "transposition" of a vector iz matrix. In the statistical model, the dependent variable y, thus, is a random variable for which we obtain measurements that contain some "noise" or measurement errors that are captured in the error terms e. On the other hand, whwn the numerical problem that we are facing, it can be what is used when the sum of deviations from the average should be least.
Least squares regression L 2 norm is what is used when the sum of deviations from the average should be least most well-known and the ahen basic form of the least squares optimization problems. So, it has found many applications in mathematics and statistical data analyses as well as the other scientific fields.
If and when the error terms in the linear regression model do indeed follow a normal distribution, then the least squares frm estimates are "the best estimators" under most acceptable criteria. Thus, this approach is particularly helpful in all those situations involving the study of large data sets, handling large samples with a consistent numbers i outliers e.
The least squares linear regression estimator is well-known to be highly sensitive to outliers in the data and as a result many more robust estimators have been proposed as alternatives. One of the earliest proposals was the least-sum of absolute deviations L 1 regression, where the regression coefficients are estimated through minimization of the sum of the absolute values of the residuals. An important advantage of L shoule regression over L 2 regression is its robustness.
For the unconstrained problem it is well known that the L 1 regression estimator can resist a few large errors in the data y Shi hte Lukas, The use of L 1 norm criterion is appropriate when it is suspected bs a small portion of the data being analyzed is unreliable i. The L 1 norm criterion has the capability of effectively ignoring a few bad data points while emphasizing the majority of data points which more properly reflect the true nature of the data Cadzow, One can see Huber for further discussion of the L 1 approach in robust estimation.
One of the most widely known techniques for errors-in-variables estimation in the simple linear regression model is orthogonal regression, also sometimes called as the functional maximum likelihood estimator under the constraint leasg known error variance ratio. In ordinary linear regression, the goal is to minimize the sum of the squared vertical distances between the y data values and the corresponding y values on the fitted line. In orthogonal lezst the goal is to minimize the orthogonal perpendicular distances from the data points to the fitted line.
This well-known orthogonal regression estimator is an old method and derived in many studies such as WeisbergCarroll and Ruppert and Leng et al. The use of orthogonal regression must include a careful assessment of equation error, and not merely the usual estimation of the ratio of measurement error variance. Thus, when its assumptions hold, the orthogonal regression is a perfectly justifiable method of estimation.
However it often lends itself to misuse by the unwary as a method, because orthogonal regression does not take equation error into account. The most serious problem with least squares regression is non-robustness to outliers. In particular, if you have one extremely bad data point, it will have a strong influence on the solution because outliers can have a large influence on the regression parameters.
A simple remedy is to iteratively discard the worst-fitting data point, and re-compute the least squares fit to the remaining data. Another approach, termed robust regression, is to employ a fitting criterion that is not as vulnerable as least squares to unusual data. The most common general method of robust regression is M-estimation, introduced by Huber Nonlinear regression models play an important role in many fields. For estimating the aveerage of a nonlinear model, the classical least squares method is commonly used eum many cases.
However, it is well known that these classical methods are often very sensitive to outliers and other departures from the underlying distribution. Most robust developments on the estimation of regression models are based on the generalizations of least squares or maximum likelihood methods. Robust regression procedure is less influenced by extreme values. In assessing the behavior of robust regression estimates, however, the techniques of small sample asymptotes can be very useful.
The literature on the robust regression dates back to Huber Some of the robust techniques are also discussed in many studies such as HuberSinha et al. One of the most important problems in regression analyses is the selection of an appropriate probability distribution for a given data set, which can give reasonably accurate and robust estimates. As shown in the literature, there is no certain rule for selecting the appropriate distribution or parameter estimation techniques for a given data set and various distributions should be applied and the best-fitting model should be selected.
Thus, choosing the best-fitting model is quite an important procedure in frequency analysis. In most cases, the selection of an appropriate ldast is based on goodness-of-fit assessment. The goodness-of-fit technique can be described as the method for examining how well sample data agree with an assumed probability distribution as its population. Several goodness-of-fit tests are developed and used in engineering decision-making.
Selection criteria among those methods, the determination of correlation coefficient R 2 or sometimes r whxt used has been known as a powerful and conceptually simple method. Although the R 2 is solely based on the covariance penalty, it plays an important role in model fit assessment. It should certainly not be used as a unique model fit assessor, but can provide a reasonable and rapid model fit indication Heo et al.
More traditionally, for leat linear regression model, a very simple fit indicator is, however, given by the determination of correlation coefficient R 2. If x is considered as random, it can be defined as the parameter that is the squared correlation between y and the best linear combination of deviationns x. The R 2 is usually presented as the quantity that estimates the percentage avdrage variance of the response variable explained by linear relationship with the explanatory variables.
It is computed by means of the ratio:. Equation 5 has a nice interpretation in avrrage R 2 measures the goodness of fit of the regression model by its ability to predict the response variable, ability measured shoould the correlation. The correlation coefficient is location and scale invariant, and statistically independent of the mean, y whfn, and standard deviation.
Essentially, R 2 measures the linearity of the probability plot, providing a quantitative assessment of fit and it is assumed that the observations could have been drawn from the fitted distribution if the value of R 2 is close to 1. Main goal of this study is to derive a relationship between the surface wave magnitude and rupture length for great earthquakes occurred in and around Turkey.
For this purpose, the earthquakes whose surface wave magnitude are greater than 5. Magnitude ranges vary from 5. These events and their detailed information can be found from different studies in the literature e. What is used when the sum of deviations from the average should be least epicenters of all earthquakes used in the analysis and whole tectonics are plotted in Figure 1.
In addition, some principle characteristics of what is the atomic theory of democritus earthquakes and geological environments around these earthquakes are briefly discussed in this section. The surface rupture pattern of this earthquake was reported as a complex with whne substantial right-lateral strike-slip component.
Nalbant et al. Geological formation around the related earthquakes is mainly composed of clastic and carbonate rocks. Another earthquake which is occurred in the same zone is the Marmara earthquake. This earthquake occurred on the southwest lrast of the Sea of Marmara, north of the Marmara Islands. Geological structure around this earthquake epicenter includes granitoid, schist, phillite, marble, metabasic rocks teh. There are veviations earthquakes in Burdur region.
The first one occurred in and the what makes good relationship one in These two earthquakes were located approximately in the same coordinates. The first earthquake occurred on the northeast segment of Burdur Fault and avwrage other occurred on the southwest of Burdur Lake. Focal mechanisms of the and Burdur earthquakes are very similar. Main geological structure around these epicenters includes undifferentiated quaternary, continental clastic rocks and undifferentiated continental clastic rocks.
As east-west normal faulting and right-lateral strike-slip faulting are found in the area, Nalbant et al. Geological structure around this earthquake epicenter is given as undifferentiated quaternary by MTA. The second earthquake in the same region is the Demirci Manisa earthquake.