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Table 1: Parameter Estimates for the Coordinating and Non-Coordinating Models

in Coordination, Moderation and Institutional Balancing in American House Elections at Midterm
by Walter R. Mebane, Jr., Jr. Y, Jasjeet S. Sekhon
"... In PAGE 28: ... For 1994 and 1998 there is a signi#0Ccant tendency for electors who have higher values of #12 i to be more likely to vote than electors who havelower values of #12 i : conservative electors were especially mobilized in those two elections. *** Table1 about here *** In every year, the coordinating model passes the parameter-based tests of the conditions neces- sary for it to describe coordinating behavior. Table 2 reports the LR test statistics for the constraint #0B = 1, imposed separately for eachyear.... In PAGE 29: ... The House position was expected to be closer to the Democratic position in 1978, 1982, 1986 and 1990, closer to the Republican position in 1994 and 1998. The MLEs for #0B in the coordinating model are less than :5inevery year except one #28see Table1 #29, suggesting that electors expected the Presidenttobeweaker than the House in determining post-midterm policy. *** Table 4 about here *** The distribution of the ordering of electors apos; ideal points with respect to the post-election policies electors expect according to the coordinating model shows that the moderating mechanism of the coordinating model is capable of generating a midterm cycle of the kind emphasized by Alesina and Rosenthal #281989; 1995#29, though it need not do so.... In PAGE 37: ... NES survey respondents mayoverreport the frequency with which they vote. Among the 9,639 cases from years 1978#7B98 that we use to compute the parameter estimates reported in Table1 , the ! i -weighted percentage reporting having voted is, by year: 47.... In PAGE 38: ... 19. Table1 shows #0B 90 , #0B 94 , #1A 78 , #1A 86 , #1A 90 and #1A 98 to have MLEs equal to either 0:0or1:0, on the conceptual boundary of the parameter space. Consequently, the asymptotic distributions of the MLEs and the LR test statistics are complicated #28Moran 1971; Self and Liang 1987#29.... In PAGE 39: ...Table1 to tabulate that mixture distribution and estimate the con#0Cdence intervals of Table 3. 20.... In PAGE 48: ...524 .455 Note: Computed using the parameter MLEs in Table1 and 1978#7B98 ANES data. Table 5: Orderings of Ideal Points and Expected PartyPolicy Positions, byYear Ordering year #12 i #3C ~ #12 Mi ; ~ #12 i ~ #12 Mi #3C#12 i #3C ~ #12 i ~ #12 i #3C#12 i #3C ~ #12 Mi ~ #12 Mi ; ~ #12 i #3C#12 i #12 Di = #12 Ri amp; i =0 1978 19.... In PAGE 48: ... Entries show the percentage of electors in eachyear who have #12 Di #3C#12 Ri and the indicated ordering of ideal point and expected policy positions, or who have #12 Di = #12 Ri , or who lack policy position values #28 amp; i = 0#29. Computed using the parameter MLEs in Table1 and 1978#7B98 ANES data. Percentages for those with #12 Di #3E#12 Ri are, byyear: #12 i #3C ~ #12 i #285.... ..."

Table 1. Maximum Likelihood Estimates

in On Statistical Behavior of Branch Coverage in Testing Behavioral VHDL Models
by Amjad Hajjar, Tom Chen, Anneliese Von Mayrhauser 2000
"... In PAGE 4: ... The rationale of this method is that it is robust, consistent, and straightforward. For a given distribution, the Maximum Likelihood Estimate (MLE) for its parameter is the mini- mum value of: log N Y i=1 f(xi; ) ! (4) Table1 lists the MLE of the parameters of the three chosen distributions. Finally, the data to be fitted out of the behavioral mod- els has to be prepared.... ..."
Cited by 2

Table 1 Maximum likelihood estimates

in Economics Letters 60 (1998) 335--341
by Scaling The Volatility, D. Canning, L. A. N. Amaral, Y. Lee, M. Meyer, H. E. Stanley
"... In PAGE 6: ...84. Looking at Table1 , the increase in the log likelihood when we move from imposing a constant variance in estimation III to include the power law term in estimation IV is very large. Twice the difference in the log likelihoods is 256.... ..."

Table 1. Maximum Likelihood Estimates

in unknown title
by unknown authors 2001
Cited by 3

TABLE 2 Maximum Likelihood Estimates

in Regional Incentives and Industrial Location in Puerto Rico
by Paulo Guimaraes, Robert J. Rolfe, Douglas P. Woodward 1998
Cited by 1

TABLE 3 Maximum likelihood estimates

in What's Causing Overreaction? An Experimental Investigation of Recency and the Hot Hand Effect
by Theo Offerman, Joep Sonnemans
Cited by 1

Table 5: Maximum Likelihood Estimates

in The Dynamics of Capital Structure: An Empirical Analysis of a Partially Observable System
by Michael Roberts

Table 6: Maximum Likelihood Estimates

in The Dynamics of Capital Structure: An Empirical Analysis of a Partially Observable System
by Michael Roberts

Table 1. Maximum Likelihood Estimates

in Permanent and Transitory Components of Recessions
by Chang-Jin Kim, Christian J. Murray

Table 1: Maximum likelihood estimations

in Dependence in non-life insurance
by Hanna Arvidsson, Sofie Francke, Hanna Arvidsson, Sofie Francke 2007
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