### Table 4 Mean Correlations Between the Latent Change and the Latent Third Variable

2000

"... In PAGE 13: ...change variable for the six conditions can be found in Table4 . The results for the proposed model were similar to the results found for the change score cor- relations; namely, the relationship between the third variables and change was unaffected by the relationship between the third variables and baseline.... ..."

### Table 1: Speaker identification errors for the Gaussian mixture model (GMM), the probabilistic latent semantic analysis model (PLSA) and the regularized probabilistic latent semantic analysis model (RPLSA). Test Data

2005

"... In PAGE 8: ... Specifically, three pieces of test speech from each speaker that have the lengths of 2, 3 or 5 seconds were used in each experiment. The results are shown in Table1 . Clearly, both PLSA and RPLSA are more effective than the GMM in all cases.... ..."

Cited by 3

### Table 1: Speaker identification errors for the Gaussian mixture model (GMM), the probabilistic latent semantic analysis model (PLSA) and the regularized probabilis- tic latent semantic analysis model (RPLSA). Test Data

2005

"... In PAGE 8: ... To compare the algorithms in a wide range we tried various lengths of test data. The results are shown in Table1 . Clearly, both PLSA and RPLSA are more effective than the GMM in all cases.... ..."

Cited by 3

### Table 1: Performance of DMA and DP models as priors in a measurement error model , from a simulation study with 50 replicates.

"... In PAGE 3: ... They showed that the unbalancedness of the partition distribution, which exists in the prior DP model, persists a posteriori, leading to a di culty of interpretation of the mixture components in the DP case. Overall similar predictive densities between DMA and DP were found for a range of density shapes, as well as in the context of modelling a latent distribution in a measurement error context (see Figure 1 and Table1 ), though in some cases the MDP model leads to higher variability of the latent variables for some extreme observations (see Figure 2). This supports exploring the use and the relative performance of mixtures with variable number of components as an alternative to some NPB constructions in a variety of contexts.... ..."

### Table 2. Correlations of Latent Variables and Evidence for Discriminant Validity

"... In PAGE 7: ... For satisfactory discriminant validity, the square root of AVE from the construct should be greater than the variance shared between the construct and other constructs in the model [4]. These items also demonstrated satisfactory convergent and discriminant validity (see Table2 ). Having validated the measurement modeling, the next step was testing the hypothesized relationships among various latent constructs in the PLS structural model.... ..."

### Table 6.1: Formal correspondence between continuous latent variable models and Bayesian inverse problem theory.

### Table 10: Evaluating contextual dependency of paraphrases by latent variable models model window independent dependent corrected

"... In PAGE 6: ... Table 9: Potential upper bound of this method human judgement human judgement from paraphrasing based on topic perspective same different independent 61 10 dependent 15 22 We prepared several latent variable models to investigate the performance of the proposed method and applied it to the sampled paraphras- ing sentences mentioned above. Table10 shows the evaluation results. 5 Discussion First, there is no major performance difference between pLSI and LDA in paraphrasing evalu- ation.... In PAGE 7: ... In addition, Table 8 reveals that judging the contextual dependency of paraphrasing pairs does not require fine-grained topics. From the results shown in Table10 , we can conclude that topic inference by latent variable models resembles context judgement by humans as recorded in error rate. However, we note that the error rate was not weighted for contextually independent or dependent results.... In PAGE 7: ... In our experiments, from the results shown in Table 9, C is set to 25. From the results shown in Table10 , we can conclude that the performance of our method is almost the same as that by the manually annotated topics, and the accuracy of our method is almost 80% for paraphrasing pairs that can be judged by contextual information. There are several possibilities for improving accuracy.... ..."

### Table 10: Evaluating contextual dependency of paraphrases by latent variable models model window independent dependent corrected

"... In PAGE 6: ... Table 9: Potential upper bound of this method human judgement human judgement from paraphrasing based on topic perspective same different independent 61 10 dependent 15 22 We prepared several latent variable models to investigate the performance of the proposed method and applied it to the sampled paraphras- ing sentences mentioned above. Table10 shows the evaluation results. 5 Discussion First, there is no major performance difference between pLSI and LDA in paraphrasing evalu- ation.... In PAGE 7: ... In addition, Table 8 reveals that judging the contextual dependency of paraphrasing pairs does not require ne-grained topics. From the results shown in Table10 , we can conclude that topic inference by latent variable models resembles context judgement by humans as recorded in error rate. However, we note that the error rate was not weighted for contextually independent or dependent results.... In PAGE 7: ... In our experiments, from the results shown in Table 9, C is set to 25. From the results shown in Table10 , we can conclude that the performance of our method is almost the same as that by the manually annotated topics, and the accuracy of our method is almost 80% for paraphrasing pairs that can be judged by contextual information. There are several possibilities for improving accuracy.... ..."

### Table 7 suggests that differences between income groups are not being caused by differential

"... In PAGE 30: ... Thus, it is possible to some extent to examine whether negative income shocks influence the attendance decision when a full tuition subsidy is in place, and to what extent the effect of income shocks differ by income. The results of the proportional hazard model with the additional change in family income variable, ) income, entered as a time varying covariate are shown in Table7 and indicate that negative income shocks have an insignificant effect on the timing of exits. Table 7 suggests that differences between income groups are not being caused by differential... In PAGE 44: ... Table7 Proportional hazard model of retention including information on family income shocks n=2446 male .220* (.... ..."