### Table 6. Nonparametric estimates using pooled data.

in Nonparametric Estimation Of Labor Supply Functions Generated By Piece Wise Linear Budget Constraints

"... In PAGE 30: ...ncome. Both the elasticity and coefficient estimates show this pattern. The nonparametric elasticity estimate is smaller than the parametric one for the wage rate and larger for nonlabor income. Also, for the nonparametric estimates in the first column of Table6 , the coefficient of w3 is smaller than is the wage coefficient for the parametric estimate in equation (14). As previously noted, the coefficient of w3 gives the wage effect for a linear budget set, because dw is identically zero in that case.... In PAGE 33: ...assuming homoskedasticity leads to a simple Hausman test of the distributional assumption. Comparing the coefficient of w3 in the first column of Table6 with the coefficient of w in the first column of Table 7 gives a Hausman statistic 6.53, that should be a realization of a standard normal distribution.... ..."

### Table 2 Variance of software reliability estimates

2004

"... In PAGE 8: ... In each run x0Z3000 tests are applied to the software under test (modeled by binomial probability distributions). Table2 tabulates the simulation results of the variance of R or r for the three testing strategies. From Table 2, we see that in the first run of simulation the adaptive testing strategy (testing strategy I) generates 5.... In PAGE 8: ... Table 2 tabulates the simulation results of the variance of R or r for the three testing strategies. From Table2 , we see that in the first run of simulation the adaptive testing strategy (testing strategy I) generates 5.570390!10K9 as the variance of software reliability estimate, and the mean value of the variance in the eight simulation runs is 6.... ..."

### Table 10: Pinball loss comparison between the nonparametric quantile regression without (npqr) and with (npqrm) monotonicity constraints.

2006

"... In PAGE 27: ... Note that on the engines data set the monotonicity constraint is not perfectly satisfied. Table10 shows the average pinball loss comparison between the nonparametric quantile regression without (npqr) and with (npqrm) monotonicity constraints. See above for the notation of the table.... ..."

Cited by 7

### Table 1: Software Reliability Models

"... In PAGE 3: ... Type : Distribution of the number of failures ex- perienced by time t. From the various models in Table1 , Actually NHPP(Non Homogeneous Poisson Process) model is used for software test data or operational data set but this model can also be useful for dong an analysis of reliability with performance criterion. This paper uses NHPP model and provides per- formance reliability criterion.... In PAGE 3: ...2.3 RELIABILITY GROWTH MODEL WITH PERFORMANCE FACTORS This model is based on hardware performance data accord- ing to the exponential class in Table1 and its formulation is as follows: s s I W K H 0 0 = l failure per CPU second (5) Finite failure category Poisson type Binomial type Other types Exponential class Musa Moranda Schneidewind Goel-Okumoto Jelinski- Moranda Shooman Goel-Okumoto Musa Keiller-Littlewood Weibull class Schick- Wolverton Pareto class Littlewood Gamma class Yamada Infinite failure category Type 1 Type 2 Type 3 Poisson Type Geometric family Moranda Musa- Okumoto Inverse linear Family Littlewood Inverse polynomial class Verral Power family Crow where, s H : Main processor speed(instructions/sec) K : Failure exposure ratio Table 1: Software Reliability Models ... ..."

### TABLE IX ESTIMATED PREDICTION ERRORS AND VARIANCES OF THE MODELS DERIVED FROM THE APPROACH FOR PARTIAL MONOTONICITY (PARTMON), STANDARD NEURAL NETWORKS WITH WEIGHT DECAY (NNETS), AND PARTIALLY MONOTONE LINEAR MODELS (PMONLIN) FOR ABALONE DATA

### Table 9 Fit of models to group choice proportions (Experiment 2) Model Estimated parameter abc Prior CPT(1) 0.323 (0.88) (0.61)

2005

"... In PAGE 15: ... Fit of parametric models to Experiment 2 The parametric models were fit to the combined data of Experiment 2 to minimize the sum of squared discrep- ancies between predicted and observed choice propor- tions. The results are shown in Table9 , which also lists choices where the models failed to predict the modal choices. Although SWU(4) model (4 free parameters) achieved a better fit than prior TAX(1), SWU cannot account for violations of restricted branch independence nor can it account for the small, but significant viola- tions of branch-splitting independence.... ..."

### Table 1: Nonparametric Lag Selection for Lynx Data

2000

"... In PAGE 14: ... We follow the suggested procedure of the last section and use only the CAF P E1 and the CAF P E2a criteria and for reasons of comparison, the linear Schwarz criterion ARSC. Table1 summarizes the results for the lynx data. Except for the CAF P E1 criterion all criteria include lag 1 and 2 in their selection.... In PAGE 14: ... Recalling the results of the previous section, these lags for the CAF P E2a may be due to over tting. To decide whether the more parsimonious model is su cient, we investigated the residuals of all suggested models using the bandwidths of Table1 and conclude that lags 1 and 2 are su cient. A plot of the estimated regression function on a relevant grid is shown in Figure 5.... In PAGE 15: ...and 3 using AF P E1 while Yao and Tong (1994) found lags 1, 3 and 6 using cross-validation. Insert Table1 about here Applying our methods to daily exchange rate data poses a di erent challenge. While there are plenty of data (3212 observations), this bene t is compromised as the data is known to be highly dependent (although only weakly correlated) and therefore asymptotics kick in very slowly.... ..."

Cited by 7

### Table 2: Software failure prediction model.

2005

"... In PAGE 6: ...Table 2: Software failure prediction model. We used the parameter values from Table2 estimated from data on the old release and predictors for the customers deploying the new release to predict the probability of a customer experiencing a failure that leads to a software change. Customers with predicted probability of failure above a certain cutoff value c are predicted to experience a software related failure.... ..."

Cited by 12

### Table 1: Mean absolute estimation error for compared methods (LocLin/Mon = Local lin- ear regression and monotonization, MonRS = Monotone regression splines, MonBayes = Monotone nonparametric Bayes estimate), based on 2000 simulations.

2008