Why Normality assumption in linear regressionProbability of x given past data and linear model...
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Why Normality assumption in linear regression
Probability of x given past data and linear model assumptionNormality assumption in linear regressionIs it necessary to plot histogram of dependent variable before running simple linear regression?Assumptions behind simple linear regression modelOLS vs. maximum likelihood under Normal distribution in linear regressionfrom where the error in target variable comes in linear regressionWhy linear regression has assumption on residual but generalized linear model has assumptions on response?Distribution of $(n-2)MSres/sigma^2$ in simple linear regressionHomoscedasticity assumption in simple linear regressionWhat if the Error is Not Normal in Linear Regression?
$begingroup$
My question is very simple: why we choose normal as the distribution that error term follows in the assumption of linear regression? Why we don't choose others like uniform, t or whatever?
regression mathematical-statistics normal-distribution error linear
asked 2 hours ago
Master ShiMaster Shi
161
$endgroup$
add a comment |
$begingroup$
My question is very simple: why we choose normal as the distribution that error term follows in the assumption of linear regression? Why we don't choose others like uniform, t or whatever?
regression mathematical-statistics normal-distribution error linear
asked 2 hours ago
Master ShiMaster Shi
161
$endgroup$
$begingroup$
We don't choose the normal assumption. It just happens to be the case that when the error is normal, the model coefficients exactly follow a normal distribution and an exact F-test can be used to test hypotheses about them.
$endgroup$
– AdamO
1 hour ago
$begingroup$
Because the math works out easily enough that people could use it before modern computers.
$endgroup$
– Nat
1 hour ago
add a comment |
$begingroup$
My question is very simple: why we choose normal as the distribution that error term follows in the assumption of linear regression? Why we don't choose others like uniform, t or whatever?
regression mathematical-statistics normal-distribution error linear
asked 2 hours ago
Master ShiMaster Shi
161
$endgroup$
My question is very simple: why we choose normal as the distribution that error term follows in the assumption of linear regression? Why we don't choose others like uniform, t or whatever?
regression mathematical-statistics normal-distribution error linear
regression mathematical-statistics normal-distribution error linear
asked 2 hours ago
Master ShiMaster Shi
161
asked 2 hours ago
Master ShiMaster Shi
161
asked 2 hours ago
Master ShiMaster Shi
161
asked 2 hours ago
Master ShiMaster Shi
161
asked 2 hours ago
Master ShiMaster Shi
161
161
$begingroup$
We don't choose the normal assumption. It just happens to be the case that when the error is normal, the model coefficients exactly follow a normal distribution and an exact F-test can be used to test hypotheses about them.
$endgroup$
– AdamO
1 hour ago
$begingroup$
Because the math works out easily enough that people could use it before modern computers.
$endgroup$
– Nat
1 hour ago
add a comment |
$begingroup$
We don't choose the normal assumption. It just happens to be the case that when the error is normal, the model coefficients exactly follow a normal distribution and an exact F-test can be used to test hypotheses about them.
$endgroup$
– AdamO
1 hour ago
$begingroup$
Because the math works out easily enough that people could use it before modern computers.
$endgroup$
– Nat
1 hour ago
$begingroup$
We don't choose the normal assumption. It just happens to be the case that when the error is normal, the model coefficients exactly follow a normal distribution and an exact F-test can be used to test hypotheses about them.
$endgroup$
– AdamO
1 hour ago
$begingroup$
We don't choose the normal assumption. It just happens to be the case that when the error is normal, the model coefficients exactly follow a normal distribution and an exact F-test can be used to test hypotheses about them.
$endgroup$
– AdamO
1 hour ago
$begingroup$
Because the math works out easily enough that people could use it before modern computers.
$endgroup$
– Nat
1 hour ago
$begingroup$
Because the math works out easily enough that people could use it before modern computers.
$endgroup$
– Nat
1 hour ago
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
You can choose another error distribution; they basically just change the loss function.
This is certainly done.
Laplace (double exponential errors) correspond to least absolute deviations regression/$L_1$ regression (which numerous posts on site discuss). Regressions with t-errors are occasionally used (in some cases because they're more robust to gross errors), though they can have a disadvantage -- the likelihood (and therefore the negative of the loss) can have multiple modes.
Uniform errors correspond to an $L_infty$ loss (minimize the maximum deviation); such regression is sometimes called Chebyshev approximation (though beware, since there's another thing with essentially the same name). Again, this is sometimes done (indeed for simple regression and smallish data sets with bounded errors with constant spread the fit is often easy enough to find by hand, directly on a plot, though in practice you can use linear programming methods, or other algorithms; indeed, $L_infty$ and $L_1$ regression problems are duals of each other, which can lead to sometimes convenient shortcuts for some problems).
Many other choices are possible and quite a few have been used in practice.
[Note that if you have additive, independent, constant-spread errors with a density of the form $k,exp(-c.g(varepsilon))$, maximizing the likelihood will correspond to minimizing $sum_i g(e_i)$, where $e_i$ is the $i$th residual.]
answered 1 hour ago
Glen_b♦Glen_b
212k22409758
$endgroup$
add a comment |
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1 Answer
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1 Answer
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$begingroup$
You can choose another error distribution; they basically just change the loss function.
This is certainly done.
Laplace (double exponential errors) correspond to least absolute deviations regression/$L_1$ regression (which numerous posts on site discuss). Regressions with t-errors are occasionally used (in some cases because they're more robust to gross errors), though they can have a disadvantage -- the likelihood (and therefore the negative of the loss) can have multiple modes.
Uniform errors correspond to an $L_infty$ loss (minimize the maximum deviation); such regression is sometimes called Chebyshev approximation (though beware, since there's another thing with essentially the same name). Again, this is sometimes done (indeed for simple regression and smallish data sets with bounded errors with constant spread the fit is often easy enough to find by hand, directly on a plot, though in practice you can use linear programming methods, or other algorithms; indeed, $L_infty$ and $L_1$ regression problems are duals of each other, which can lead to sometimes convenient shortcuts for some problems).
Many other choices are possible and quite a few have been used in practice.
[Note that if you have additive, independent, constant-spread errors with a density of the form $k,exp(-c.g(varepsilon))$, maximizing the likelihood will correspond to minimizing $sum_i g(e_i)$, where $e_i$ is the $i$th residual.]
answered 1 hour ago
Glen_b♦Glen_b
212k22409758
$endgroup$
add a comment |
$begingroup$
You can choose another error distribution; they basically just change the loss function.
This is certainly done.
Laplace (double exponential errors) correspond to least absolute deviations regression/$L_1$ regression (which numerous posts on site discuss). Regressions with t-errors are occasionally used (in some cases because they're more robust to gross errors), though they can have a disadvantage -- the likelihood (and therefore the negative of the loss) can have multiple modes.
Uniform errors correspond to an $L_infty$ loss (minimize the maximum deviation); such regression is sometimes called Chebyshev approximation (though beware, since there's another thing with essentially the same name). Again, this is sometimes done (indeed for simple regression and smallish data sets with bounded errors with constant spread the fit is often easy enough to find by hand, directly on a plot, though in practice you can use linear programming methods, or other algorithms; indeed, $L_infty$ and $L_1$ regression problems are duals of each other, which can lead to sometimes convenient shortcuts for some problems).
Many other choices are possible and quite a few have been used in practice.
[Note that if you have additive, independent, constant-spread errors with a density of the form $k,exp(-c.g(varepsilon))$, maximizing the likelihood will correspond to minimizing $sum_i g(e_i)$, where $e_i$ is the $i$th residual.]
answered 1 hour ago
Glen_b♦Glen_b
212k22409758
$endgroup$
add a comment |
$begingroup$
You can choose another error distribution; they basically just change the loss function.
This is certainly done.
Laplace (double exponential errors) correspond to least absolute deviations regression/$L_1$ regression (which numerous posts on site discuss). Regressions with t-errors are occasionally used (in some cases because they're more robust to gross errors), though they can have a disadvantage -- the likelihood (and therefore the negative of the loss) can have multiple modes.
Uniform errors correspond to an $L_infty$ loss (minimize the maximum deviation); such regression is sometimes called Chebyshev approximation (though beware, since there's another thing with essentially the same name). Again, this is sometimes done (indeed for simple regression and smallish data sets with bounded errors with constant spread the fit is often easy enough to find by hand, directly on a plot, though in practice you can use linear programming methods, or other algorithms; indeed, $L_infty$ and $L_1$ regression problems are duals of each other, which can lead to sometimes convenient shortcuts for some problems).
Many other choices are possible and quite a few have been used in practice.
[Note that if you have additive, independent, constant-spread errors with a density of the form $k,exp(-c.g(varepsilon))$, maximizing the likelihood will correspond to minimizing $sum_i g(e_i)$, where $e_i$ is the $i$th residual.]
answered 1 hour ago
Glen_b♦Glen_b
212k22409758
$endgroup$
You can choose another error distribution; they basically just change the loss function.
This is certainly done.
Laplace (double exponential errors) correspond to least absolute deviations regression/$L_1$ regression (which numerous posts on site discuss). Regressions with t-errors are occasionally used (in some cases because they're more robust to gross errors), though they can have a disadvantage -- the likelihood (and therefore the negative of the loss) can have multiple modes.
Uniform errors correspond to an $L_infty$ loss (minimize the maximum deviation); such regression is sometimes called Chebyshev approximation (though beware, since there's another thing with essentially the same name). Again, this is sometimes done (indeed for simple regression and smallish data sets with bounded errors with constant spread the fit is often easy enough to find by hand, directly on a plot, though in practice you can use linear programming methods, or other algorithms; indeed, $L_infty$ and $L_1$ regression problems are duals of each other, which can lead to sometimes convenient shortcuts for some problems).
Many other choices are possible and quite a few have been used in practice.
[Note that if you have additive, independent, constant-spread errors with a density of the form $k,exp(-c.g(varepsilon))$, maximizing the likelihood will correspond to minimizing $sum_i g(e_i)$, where $e_i$ is the $i$th residual.]
answered 1 hour ago
Glen_b♦Glen_b
212k22409758
edited 14 mins ago
answered 1 hour ago
Glen_b♦Glen_b
212k22409758
answered 1 hour ago
Glen_b♦Glen_b
212k22409758
answered 1 hour ago
Glen_b♦Glen_b
212k22409758
212k22409758
add a comment |
add a comment |
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$begingroup$
We don't choose the normal assumption. It just happens to be the case that when the error is normal, the model coefficients exactly follow a normal distribution and an exact F-test can be used to test hypotheses about them.
$endgroup$
– AdamO
1 hour ago
$begingroup$
Because the math works out easily enough that people could use it before modern computers.
$endgroup$
– Nat
1 hour ago