Missing Values, UNTIE, and Hypothesis Tests

The TRANSREG Procedure

Missing Values, UNTIE, and Hypothesis Tests

The TRANSREG procedure has the ability to estimate missing data and monotonically transform variables while untying tied values. Estimates of ordinary missing values (.) may all be different. Analyses with UNTIE transformations, the UNTIE= a-option, and ordinary missing data estimation are all prone to degeneracy problems. Consider the following example. A perfect fit is found by collapsing all observations except the one with two missing values into a single value in Y and X1.

   data x;
      input y x1 x2 @@;
      datalines;
   1 3 7    8 3 9    1 8 6    . . 9    3 3 9
   8 5 1    6 7 3    2 7 2    1 8 2    . 9 1
   ;

   proc transreg dummy;
      model linear(y) = linear(x1 x2);
      output;
   run;

   proc print;
   run;

Obs	_TYPE_	_NAME_	y	Ty	Intercept	x1	x2	TIntercept	Tx1	Tx2
1	SCORE	ROW1	1	2.7680	1	3	7	1	5.1233	7
2	SCORE	ROW2	8	2.7680	1	3	9	1	5.1233	9
3	SCORE	ROW3	1	2.7680	1	8	6	1	5.1233	6
4	SCORE	ROW4	.	12.5878	1	.	9	1	12.7791	9
5	SCORE	ROW5	3	2.7680	1	3	9	1	5.1233	9
6	SCORE	ROW6	8	2.7680	1	5	1	1	5.1233	1
7	SCORE	ROW7	6	2.7680	1	7	3	1	5.1233	3
8	SCORE	ROW8	2	2.7680	1	7	2	1	5.1233	2
9	SCORE	ROW9	1	2.7680	1	8	2	1	5.1233	2
10	SCORE	ROW10	.	2.7680	1	9	1	1	5.1233	1

Figure 65.7: Missing Values Example

Generally, the use of ordinary missing data estimation, the UNTIE transformation, and the UNTIE= a-option should be avoided, particularly with hypothesis tests. With these options, parameters are estimated based on only a single observation, and they can exert tremendous influence over the results. Each of these parameters has one model degree of freedom associated with it, so small or zero error degrees of freedom can also be a problem.

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