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Experimental Approaches to the Study of Personality
"... A review of the use of experimental techniques to develop and test theories of personality processes. Threats to valid inference including problems of scaling, reliability, and unintended confounds are considered. Basic experimental designs are discussed as ways of eliminating some, but not all thre ..."
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A review of the use of experimental techniques to develop and test theories of personality processes. Threats to valid inference including problems of scaling, reliability, and unintended confounds are considered. Basic experimental designs are discussed as ways of eliminating some, but not all threats to validity. A number of basic analytical procedures are demonstrated using simulated data that can be accessed from the web based appendix. Personality is an abstraction used to explain consistency and coherency in an individuals pattern of affects, cognitions, desires and behaviors. What one feels, thinks, wants and does changes from moment to moment and from situation to situation but shows a patterning across situations and over time that may be used to recognize, describe and even to understand a person. The task of the personality researcher is to identify the consistencies and differences within and between individuals (what one feels, thinks, wants and does) and eventually to try to explain them in terms of set of testable hypotheses (why one feels, thinks, wants and does). Personality research is the last refuge of the generalist in psychology: it requires a familiarity with the mathematics of personality measurement, an understanding of genetic mechanisms and physiological systems as they interact with environmental influences to lead to development over the life span, an appreciation of how to measure and manipulate affect and cognitive states, and an ability to integrate all of this into a coherent description of normal and abnormal behavior across situations and across time. Although the study of personality is normally associated with correlational techniques relating responses or observations in one situation or at one time with responses in other situations and other times, it is also possible to examine causal relations through
Anthropogenic Noise and its Effect on Animal Communication: An Interface Between Comparative Psychology and Conservation Biology
"... Conservation biology and comparative psychology rarely intersect, in part because conservation biology typically emphasizes populations whereas comparative psychology concentrates on individual organisms. However, both fields could benefit from their integration. Conservation biology can profit from ..."
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Conservation biology and comparative psychology rarely intersect, in part because conservation biology typically emphasizes populations whereas comparative psychology concentrates on individual organisms. However, both fields could benefit from their integration. Conservation biology can profit from an enhanced understanding of individuallevel impacts of habitat alteration and the resulting implications for conservation mitigation strategies. Comparative psychology can gain from increased attention to the mechanisms of adjustment used by organisms to “in vivo experiments ” created by anthropogenic change. In this paper, we describe a conceptual framework useful for applying our understanding of animal communication to conservation biology. We then review studies of animal communication with conservation implications, and report our own preliminary work that demonstrates our framework in action. Studies that attempt to synthesize the fields of animal behavior and conservation biology have historically been underrepresented in the journals of both fields (Sutherland, 1998), in part because these fields emphasize different levels of biological organization. Whereas animal behavior typically focuses on understanding the behavior of individuals, conservation biology more often attempts to account
MiniJudge: Software for SmallScale Experimental Syntax
"... MiniJudge is free online opensource software to help theoretical syntacticians collect and analyze nativespeaker acceptability judgments in a way that combines the speed and ease of traditional introspective methods with the power and statistical validity afforded by rigorous experimental protocol ..."
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MiniJudge is free online opensource software to help theoretical syntacticians collect and analyze nativespeaker acceptability judgments in a way that combines the speed and ease of traditional introspective methods with the power and statistical validity afforded by rigorous experimental protocols. This paper shows why MiniJudge is useful, what it feels like to use it, and how it works.
Multivariate Bernoulli Distribution Models
, 2012
"... First and most importantly, I would like to express my deepest gratitude toward my advisor Professor Grace Wahba. Her guidance and encouragement through my PhD study into various statistical machine learning methods is the key factor to the success of this dissertation. Grace is a brilliant and pass ..."
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First and most importantly, I would like to express my deepest gratitude toward my advisor Professor Grace Wahba. Her guidance and encouragement through my PhD study into various statistical machine learning methods is the key factor to the success of this dissertation. Grace is a brilliant and passionate statistician, and her insightful ideas in both statistical theories and applications inspire me. It is a great honor and privilege to have the opportunity to work closely and learn from her. This work is also the product of collaboration with a number of researchers. In particular, I would like to thank Professor Stephen Wright from Department of Computer Science for his guidance in computation. Without him, the proposed models in this thesis would not be solved with efficient optimization techniques. In addition, I am grateful to other professors in my thesis committee. I benefit from Professor Sündüz Keles ’ expertise in biostatistics and her valuable ideas in the Thursday group. On the other hand, Professor Peter Qian and Sijian Wang raised questions with deep perception and helped greatly improve the thesis. I am also greatly influenced by Professor Karl Rohe and Xinwei Deng for their improving suggestion to this work. ii I want to thank Xiwen Ma and Shilin Ding for their effort on our collaborative
Delineating the Average Rate of Change in Longitudinal Models
"... The average rate of change is a concept that has been misunderstood in the literature. This article attempts to clarify the concept and show unequivocally the mathematical definition and meaning of the average rate of change in longitudinal models. The slope from the straightline change model has a ..."
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The average rate of change is a concept that has been misunderstood in the literature. This article attempts to clarify the concept and show unequivocally the mathematical definition and meaning of the average rate of change in longitudinal models. The slope from the straightline change model has at times been interpreted as if it were always the average rate of change. It is shown, however, that this is generally not the case and holds true in only a limited number of situations. General equations are presented for two measures of discrepancy when the slope from the straightline change model is used to estimate the average rate of change. The importance of fitting an appropriate individual change model is discussed, as are the benefits provided by models nonlinear in their parameters for longitudinal data. An empirical data set is used to illustrate the analytic developments.
Statistical Growth Modeling of Longitudinal DTMRI for Regional Characterization of Early Brain Development
"... Abstract. This paper presents a framework for modeling growth trajectories and determining significant regional differences in growth pattern characteristics applied to longitudinal neuroimaging data. We use nonlinear mixed effect modeling where temporal change is modeled by the Gompertz function. T ..."
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Abstract. This paper presents a framework for modeling growth trajectories and determining significant regional differences in growth pattern characteristics applied to longitudinal neuroimaging data. We use nonlinear mixed effect modeling where temporal change is modeled by the Gompertz function. The Gompertz function uses intuitive parameters related to delay, rate of change, and expected asymptotic value; all descriptive measures which can answer clinical questions related to growth. Our proposed framework combines nonlinear modeling of individual trajectories, population analysis, and testing for regional differences. We apply this framework to the study of early maturation in white matter regions as measured with diffusion tensor imaging (DTI). Regional differences between anatomical regions of interest that are known to mature differently are analyzed and quantified. Although our framework can be applied to any imagederived measurements, we show statistical tests for axial diffusivity (AD) and radial diffusivity (RD) measurements as these are known to be sensitive to degree of myelination and axonal structuring. Experiments with image data from a large ongoing clinical study show that our framework provide descriptive, quantitative information on growth trajectories that can be directly interpreted by clinicians. To our knowledge, this is the first statistical analysis of growth functions to explain the trajectory of early brain maturation. 1
Nonlinear Change Models in Populations with Unobserved Heterogeneity
"... Abstract. When unobserved heterogeneity exists in populations where the phenomenon of interest is governed by a functional form of change linear in its parameters, the growth mixture model (GMM) is useful for modeling change conditional on latent class. However, when the functional form of interest ..."
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Abstract. When unobserved heterogeneity exists in populations where the phenomenon of interest is governed by a functional form of change linear in its parameters, the growth mixture model (GMM) is useful for modeling change conditional on latent class. However, when the functional form of interest is nonlinear in its parameters, the GMM is not very useful because it is based on a system of equations linear in its parameters. The nonlinear change mixture model (NCMM) is proposed, which explicitly addresses unobserved heterogeneity in situations where change follows a nonlinear functional form. Due to the integration of nonlinear multilevel models and finite mixture models, neither of which generally have closed form solutions, analytic solutions do not generally exist for the NCMM. Five methods of parameter estimation are developed and evaluated with a comprehensive Monte Carlo simulation study. The simulation showed that the parameters of the NCMM can be accurately estimated with several of the proposed methods, and that the method of choice depends on the precise question of interest.
Nonlinear Change Models in Heterogeneous Populations When Class Membership is Unknown: The Latent Classification Differential Change Model
"... Abstract — When unobserved heterogeneity exists in populations where the phenomenon of interest is governed by a functional form of change linear in its parameters, the growth mixture model (GMM) is extremely useful for modeling change conditional on latent class (Muthén, 2001a; Muthén, 2001b; Muthé ..."
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Abstract — When unobserved heterogeneity exists in populations where the phenomenon of interest is governed by a functional form of change linear in its parameters, the growth mixture model (GMM) is extremely useful for modeling change conditional on latent class (Muthén, 2001a; Muthén, 2001b; Muthén, 2002). However, when the functional form of interest is nonlinear in its parameters, the GMM is not very useful because it is based on a system of equations linear in its parameters. The latent classification differential change (LCDC) model is proposed and developed so that unobserved heterogeneity can be modeled in populations where the phenomenon of interest is governed by functional forms of change nonlinear in its parameters. Due to the integration of nonlinear multilevel models and finite mixture models, neither or which have closed form solutions, analytic solutions do not generally exist for the LCDC model. Five methods of parameter estimation are developed and evaluated with a comprehensive Monte Carlo simulation study. The simulation showed that the parameters of the LCDC model can be accurately estimated with each of the proposed methods, and that the method of choice depends on the precise question of interest. Index Terms — longitudinal data analysis, analysis of change, growth modeling, growth mixture modeling, heterogeneous population, heterogeneous change, nonlinear growth models, nonlinear change models, functional form of growth, functional form of change I.
Water quality in the River Clyde: a case study
, 2006
"... this paper a local linear method of smoothing is used. This method is well described, and its properties discussed, by Fan & Gijbels (1996) and many other authors. The simple idea is to estimate the regression function m(x) in the model y i = m(x i )+# i based on data {(x i , y i ); i = 1, . . . , n ..."
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this paper a local linear method of smoothing is used. This method is well described, and its properties discussed, by Fan & Gijbels (1996) and many other authors. The simple idea is to estimate the regression function m(x) in the model y i = m(x i )+# i based on data {(x i , y i ); i = 1, . . . , n} by minimising the weighted sumofsquares i=1 {y i  #  #(x i  x)} w(x i  x; h), over the parameters # and #. The weight function w ensures that the observations closest to x have most influence in the local regression. The degree of influence, and therefore the smoothness of the estimate, is controlled by the parameter h. A convenient choice of w is a normal density function with standard deviation h. The estimate is defined as the minimising value of #. Since this arises as the solution to a weighted linear regression problem, the resulting estimate m(x) can be represented as a linear combination of the elements of the response vector y and the vector m of fitted values at the covariate values x i can be represented as Sy, where the smoothing matrix S is an nn matrix of known constants. By analogy with linear models, where the number of degrees of freedom can be identified by the trace of the `hat' matrix, the approximate number of degrees of freedom for the smoothing procedure is defined as tr {S}. An appropriate setting for this value provides an attractive way of specifying the amount of smoothing applied to the data, as it determines in turn the corresponding value of the smoothing parameter h. The models fitted here have used 6 degrees of freedom for each nonparametric 7 component. This provides a good degree of flexibility beyond a linear shape, while guarding against the appearance of too much random fluctuation in the estimates