Familial data occur when observations are taken on multiple members of the same family. Due to relationships between these members, both genetic and by cohabitation, their response variables will likely exhibit some form of dependence. Most of the existing literature models this dependence with an equicorrelated structure. This structure is appropriate when the dependencies between family members are similar, such as in genetic studies, but not in cases where we expect the dependencies to differ, such as behavioral comparisons across different age groups. In this dissertation we first discuss an alternative structure based upon first-order autoregressive correlation. Specifically we create and compare various estimators based on existing and emerging methods of estimation. Asymptotic and small-sample properties are discussed, as is hypothesis testing.
The second part of this dissertation involves a slightly more complicated version of autoregressive familial correlation, where we now model heterogeneous intra-class variances. Again we create and compare various estimators and discuss both their asymptotic and small-sample properties.
In the final part of this dissertation we discuss the nuclear family model, basing the familial dependence on an equicorrelated structure. Note that while this correlation structure has been extensively studied in the case of heterogeneous variance, we model homogenous variance and use a new method for estimating the parameters. Noteworthy here is that we apply a linear transformation to simplify both the correlation matrix and the correlation parameter estimators. As before, we generate estimators and compare their asymptotic performance.
","abstract_html":"<p>Familial data occur when observations are taken on multiple members of the same family. Due to relationships between these members, both genetic and by cohabitation, their response variables will likely exhibit some form of dependence. Most of the existing literature models this dependence with an equicorrelated structure. This structure is appropriate when the dependencies between family members are similar, such as in genetic studies, but not in cases where we expect the dependencies to differ, such as behavioral comparisons across different age groups. In this dissertation we first discuss an alternative structure based upon first-order autoregressive correlation. Specifically we create and compare various estimators based on existing and emerging methods of estimation. Asymptotic and small-sample properties are discussed, as is hypothesis testing.</p> <p>The second part of this dissertation involves a slightly more complicated version of autoregressive familial correlation, where we now model heterogeneous intra-class variances. Again we create and compare various estimators and discuss both their asymptotic and small-sample properties.</p> <p>In the final part of this dissertation we discuss the nuclear family model, basing the familial dependence on an equicorrelated structure. Note that while this correlation structure has been extensively studied in the case of heterogeneous variance, we model homogenous variance and use a new method for estimating the parameters. Noteworthy here is that we apply a linear transformation to simplify both the correlation matrix and the correlation parameter estimators. As before, we generate estimators and compare their asymptotic performance.</p>","abstract_has_math":false,"creators":["Sabo, Roy"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Mathematics & Statistics","degree_department":null,"school":null,"contributors":["N. Rao Chaganty","Dayanand Naik","Larry Lee","Larry Filer"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007-01-01T08:00:00Z","date_published":"2007-01-01T08:00:00Z","updated_at":"2026-07-24T03:35:00Z","subjects":["Autoregressive correlation","Familial correlation","Intrafamily correlations","Maximum-likelihood estimation","Quasi-least squares","Applied Statistics","Probability","Statistical Models"],"languages":[],"rights":["In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["9780549320418"],"render_values":[{"text":"9780549320418","href":null,"code":true}]}]},"links":{"outbound_url":"https://digitalcommons.odu.edu/mathstat_etds/53","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["N. Rao Chaganty","Dayanand Naik","Larry Lee","Larry Filer"]},{"key":"dc:creator","label":"Author","values":["Sabo, Roy"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-06-13T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mathematics & Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Autoregressive correlation","Familial correlation","Intrafamily correlations","Maximum-likelihood estimation","Quasi-least squares","Applied Statistics","Probability","Statistical Models"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
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The second part of this dissertation involves a slightly more complicated version of autoregressive familial correlation, where we now model heterogeneous intra-class variances. Again we create and compare various estimators and discuss both their asymptotic and small-sample properties.
In the final part of this dissertation we discuss the nuclear family model, basing the familial dependence on an equicorrelated structure. Note that while this correlation structure has been extensively studied in the case of heterogeneous variance, we model homogenous variance and use a new method for estimating the parameters. Noteworthy here is that we apply a linear transformation to simplify both the correlation matrix and the correlation parameter estimators. As before, we generate estimators and compare their asymptotic performance.
"]},{"key":"dc:title","label":"Title","values":["Modeling and Efficient Estimation of Intra-Family Correlations"]}]}],"canonical_facts":{"dc:contributor":["N. Rao Chaganty","Dayanand Naik","Larry Lee","Larry Filer"],"dc:creator":["Sabo, Roy"],"dc:date.available":["2019-06-13T07:00:00Z"],"dc:description.abstract":["Familial data occur when observations are taken on multiple members of the same family. Due to relationships between these members, both genetic and by cohabitation, their response variables will likely exhibit some form of dependence. Most of the existing literature models this dependence with an equicorrelated structure. This structure is appropriate when the dependencies between family members are similar, such as in genetic studies, but not in cases where we expect the dependencies to differ, such as behavioral comparisons across different age groups. In this dissertation we first discuss an alternative structure based upon first-order autoregressive correlation. Specifically we create and compare various estimators based on existing and emerging methods of estimation. Asymptotic and small-sample properties are discussed, as is hypothesis testing.
The second part of this dissertation involves a slightly more complicated version of autoregressive familial correlation, where we now model heterogeneous intra-class variances. Again we create and compare various estimators and discuss both their asymptotic and small-sample properties.
In the final part of this dissertation we discuss the nuclear family model, basing the familial dependence on an equicorrelated structure. Note that while this correlation structure has been extensively studied in the case of heterogeneous variance, we model homogenous variance and use a new method for estimating the parameters. Noteworthy here is that we apply a linear transformation to simplify both the correlation matrix and the correlation parameter estimators. As before, we generate estimators and compare their asymptotic performance.
"],"dc:identifier":["9780549320418","https://digitalcommons.odu.edu/mathstat_etds/53"],"dc:rights":["In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
"],"dc:subject":["Autoregressive correlation","Familial correlation","Intrafamily correlations","Maximum-likelihood estimation","Quasi-least squares","Applied Statistics","Probability","Statistical Models"],"dc:title":["Modeling and Efficient Estimation of Intra-Family Correlations"],"thesis:degree_discipline":["Mathematics & Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:35:00Z"}