References
- Cook RD. Detection of influential observation in linear regression. Technometrics. 1977;19:15–18.
- Belsey DA, Kuh E, Welsch RE. Regression diagnostics. New York: Wiley; 1980.
- Caroni C. Residuals and influence in the multivariate linear model. Stat. 1987;36:365–370.
- Hossain A, Naik DN. Detection of influential observations in multivariate regression. J Appl Stat. 1989;16:25–37.
- Barrett BE, Ling RF. General classes of influence measures for multivariate regression. J Amer Statist Assoc. 1992;87:184–191.
- Hadi AS, Jones WD, Ling RF. A unifying representation of some case-deletion influence measures in univariate and multivariate linear regression. J Statist Plann Inference. 1995;46:123–135.
- Radhakrishnan A, Kshirsagar AM. Influence functions for certain parameters in multivariate analysis. Comm Stat Theor Methods. 1981;10:515–529.
- Critchley F. Influence in principal components analysis. Biometrika. 1985;72:627–636.
- Tanaka Y. Sensitivity analysis in principal component analysis: influence on the subspace spanned by principal components. Comm Stat Theor Methods. 1988;17:3157–3175.
- Benasseni J. Sensitivity of principal component analysis to data perturbation. In: Diday E, editor. Data analysis and informatics. V. New York: Elsevier; 1988. p. 303–310.
- Pack P, Jolliffe IT, Morgan BJT. Influential observations in principal component analysis: a case-study. J Appl Stat. 1988;15:39–52.
- Brooks SP. Diagnostics for principal components – influence functions as diagnostic tools. Stat. 1994;43:483–494.
- Shi L. Local influence in principal components analysis. Biometrika. 1997;84:175–186.
- Tanaka Y, Zhang F, Mori Y. Local influence in principal component analysis: relationship between the local influence and influence function approaches revisited. Comp Stat Data Anal. 2003;44:143–160.
- Fung WK. Diagnostics in linear discriminant analysis. J Amer Statist Assoc. 1995;90:952–956.
- Campbell NA. The influence function as an aid in outlier detection in discriminant analysis. J R Stat Soc Ser C Appl Stat. 1978;27:251–258.
- Critchley F, Vitiello C. The influence of observations on misclassification probability estimates in linear discriminant analysis. Biometrika. 1991;78:677–690.
- Riani M, Atkinson AC. A unified approach to outliers, influence and transformations in discriminant analysis. J Comput Graph Stat. 2001;10:513–544.
- Wang X, Ren S, Shi L. Local influence in discriminant analysis. J Syst Sci Math Sci. 1995;8:27–36.
- Sibson R. Studies in the robustness of multidimensional scaling: perturbational analysis of classical scaling. J R Stat Soc Ser B Stat Methodol. 1979;41:217–229.
- Romanazzi M. Influence in canonical correlation analysis. Psychometrika. 1992;57:237–259.
- Tanaka Y, Zhang F, Yang WS. On local influence in canonical correlation analysis. Comm Stat Theor Meth. 2002;31:2325–2347.
- Pack P, Jolliffe IT. Influence in correspondence analysis. J R Stat Soc Ser C Appl Stat. 1992;41:365–380.
- Yuan KH, Bentler PM. Effect of outliers on estimators and tests in covariance structure analysis. Br J Math Stat Psychol. 2001;54:161–175.
- Tanaka Y, Watadani S, Moon SH. Influence in covariance structure analysis: with an application to confirmatory factor analysis. Comm Stat Theory Methods. 1991;20:3805–3821.
- Tanaka Y, Watadani S. Sensitivity analysis in covariance structure analysis with equality constraints. Comm Stat Theory Methods. 1992;21:1501–1515.
- Cadigan NG. Local influence in structural equation models. Struct Equ Model. 1995;2:184–191.
- Lee SY, Wang SJ. Sensitivity analysis of structural equation models. Psychometrika. 1996;61:93–108.
- Poon WY, Wang SJ, Lee SY. Influence analysis of structural equation models with polytomous variables. Psychometrika. 1999;64:461–473.
- Lee SY, Tang NS. Local influence analysis of nonlinear structural equation models. Psychometrika. 2004;69:573–592.
- Wang SJ, Lee SY. Sensitivity analysis of structural equation models with equality functional constraints. Comput Statist Data Anal. 1996;23:239–256.
- Tanaka Y, Odaka Y. Influential observations in principal factor analysis. Psychometrika. 1989;54:475–485.
- Kwan CW, Fung WK. Assessing local influence for specific restricted likelihood: application to factor analysis. Psychometrika. 1998;63:35–46.
- Jung KM, Kim MG, Kim BC. Local influence in maximum likelihood factor analysis. Comput Statist Data Anal. 1997;24:483–491.
- Lee T, MacCallum RC. Parameter influence in structural equation modeling. Struct Equ Model. 2015;22:102–114.
- Pregibon D. Data analytic methods for generalized linear models [PhD dissertation]. University of Toronto; 1979. Unpublished.
- Pregibon D. Logistic regression diagnostics. Ann Stat. 1981;9:705–724.
- Cook RD. Assessment of local influence (with discussion). J R Stat Soc Ser B Stat Methodol. 1986;48:133–169.
- Poon WY, Poon YS. Conditional local influence in case-weights linear regression. Br J Math Stat Psychol. 2001;54:177–191.
- Song XY, Lee SY. Local influence analysis of two-level latent variable models with continuous and polytomous data. Statist Sinica. 2004;14:317–332.
- Poon WY, Poon YS. Conformal normal curvature and assessment of local influence. J R Stat Soc Ser B Stat Methodol. 1999;61:51–61.
- Zhu HT, Lee SY. Local influence for incomplete data models. J R Stat Soc Ser B Stat Methodol. 2001;63:111–126.
- Lee SY, Xu L. On local influence analysis of full information item factor models. Psychometrika. 2003;68:339–360.
- Poon WY. Identifying influential observations in discriminant analysis. Stat Methods Med Res. 2004;13:291–308.
- Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B Stat Methodol. 1977;39:1–38.
- Zhu HT, Lee SY. Local influence for generalized linear mixed models. Can J Stat. 2003;31:293–309.
- Lee SY, Xu L. Influence analysis of nonlinear mixed-effects models. Comput Statist Data Anal. 2004;45:321–341.
- Birnbaum A. Some latent trait models and their use in inferring an examinee's ability. In: Lord FM, Novick MR, editors. Statistical theories of mental test scores. Reading (MA): Addison-Wesley; 1968. p. 397–424.
- Glas CAW. Maximum likelihood estimation. In: van der Linden WJ, editor. Handbook of item response theory. Volume 2: Statistical tools. Boca Raton (FL): CRC Press; 2016. p. 197–216.
- Poon W-Y, Tang M-L, Wang S-J. Influence measures in contingency tables with application in sampling zeros. Sociol Methods Res. 2003;31:439–452.
- Bock RD, Lieberman M. Fitting a response model for n dichotomously scored items. Psychometrika. 1970;35:179–197.
- Reese LM, Cotter RA. A compendium of LSAT and LSAC-sponsored item types. Newton (PA): Law School Admission Council; 1994; (Technical Report. No. LSAC-RR-94-01).
- Rizopoulos D. Package ‘ltm’. R package version 1.1-1; 2018.
- Reeve BB, Fayers P. Applying item response theory modeling for evaluating questionnaire item and scale properties. In: Reeves BB, Fayers P, editors. Assessing quality of life in clinical trials: methods and practice. Oxford: Oxford University Press; 2005. p. 55–73.
- Reise SP, Horan WP, Blanchard JJ. The challenge of fitting an item response theory model to the Social Anhedonia Scale. J Pers Assess. 2011;93:213–224.
- Duncan OD. Indicators of sex typing: traditional and egalitarian, situational and ideological responses. Am J Sociol. 1979;85:251–260.
- Mavridis D, Moustaki I. The forward search algorithm for detecting aberrant response patterns in factor analysis for binary data. J Comput Graph Stat. 2009;18:1016–1034.
- Baker SG. A simple method for computing the observed information matrix when using the EM algorithm with categorical data. J Comput Graph Stat. 1992;1:63–76.
- Liu CW, Chalmers RP. A note on computing Louis' observed information matrix identity for IRT and cognitive diagnostic models. Br J Math Stat Psychol. 2020; (in press).
- Wells CS, Hambleton RK. Model fit with residual analyses. In: van der Linden WJ, editor. Handbook of item response theory. Vol. 2, Statistical tools. Boca Raton (FL): CRC Press; 2016. p. 395–413.
- Glas CAW, Khalid N. Person fit. In: van der Linden WJ, editor. Handbook of item response theory. Vol. 2, Statistical tools. Boca Raton (FL): CRC Press; 2016. p. 107–126.
- Tendeiro JN, Meijer RR, Niessen ASM. PerFit: an R package for person fit analysis in IRT. J Stat Software. 2016;74:1–27.