Statistics and Probability Commons^™

Hierarchical Probit Models For Ordinal Ratings Data, Allison M. Butler

Theses and Dissertations

University students often complete evaluations of their courses and instructors. The evaluation tool typically contains questions about the course and the instructor on an ordinal Likert scale. We assess instructor effectiveness while adjusting for known confounders. We present a probit regression model with a latent variable to measure the instructor effectiveness accounting for student specific covariates, such as student grade in the course, high school and university GPA, and ACT score.

Targeted Maximum Likelihood Estimation Of Conditional Relative Risk In A Semi-Parametric Regression Model, Cathy Tuglus, Kristin E. Porter, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

The conditional relative risk is an important measure in medical and epidemiological studies when the outcome of interest is binary (i.e. disease vs. no disease). When the outcome is common, estimation of conditional relative risk and related parameters can be problematic, especially when the exposure or covariates are continuous. We propose a new estimation procedure based on targeted maximum likelihood methodology that targets the parameters relating to the conditional relative risk for common outcomes under a log-linear, or multiplicative, semi-parametric model. In this paper, we present three possible targeted maximum likelihood estimators for relative risk parameters implied by such a …

Probabilistic Assessment Of Drought Characteristics Using A Hidden Markov Model, Ganeshchandra Mallya, Shivam Tripathi, Sergey Kirshner, Rao S. Govindaraju

2011 Symposium on Data-Driven Approaches to Droughts

Droughts are evaluated using drought indices that measure the departure of meteorological and hydrological variables such as precipitation and stream flow from their long-term averages. While there are many drought indices proposed in the literature, most of them use pre-defined thresholds for identifying drought classes ignoring the inherent uncertainties in characterizing droughts. In this study, a hidden Markov model (HMM) [1] is developed for probabilistic classification of drought states. The HMM captures space and time dependence in the data. The proposed model is applied to assess drought characteristics in Indiana using monthly precipitation and stream flow data. The comparison of …

Super Learner Based Conditional Density Estimation With Application To Marginal Structural Models, Ivan Diaz Munoz, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In this paper we present a histogram-like estimator of a conditional density that uses super learner crossvalidation to estimate the histogram probabilities, as well as the optimal number and position of the bins. This estimator is an alternative to kernel density estimators when the dimension of the problem is large. We demonstrate its applicability to estimation of Marginal Structural Model (MSM) parameters in which an initial estimator of the treatment %mechanism is needed. MSM estimation based on the proposed density estimator results in less biased estimates, when compared to estimates based on a misspecified parametric model.

Comparing Roc Curves Derived From Regression Models, Venkatraman E. Seshan, Mithat Gonen, Colin B. Begg

Memorial Sloan-Kettering Cancer Center, Dept. of Epidemiology & Biostatistics Working Paper Series

In constructing predictive models, investigators frequently assess the incremental value of a predictive marker by comparing the ROC curve generated from the predictive model including the new marker with the ROC curve from the model excluding the new marker. Many commentators have noticed empirically that a test of the two ROC areas often produces a non-significant result when a corresponding Wald test from the underlying regression model is significant. A recent article showed using simulations that the widely-used ROC area test [1] produces exceptionally conservative test size and extremely low power [2]. In this article we show why the ROC …

U.S. Cultural Involvement And Its Association With Co-Occurring Substance Abuse And Sexual Risk Behaviors Among Youth In The Dominican Republic, Elián P. Cabrera-Nguyen, Juan B. Peña

Elián P. Cabrera-Nguyen

We examined the relationship of US cultural involvement with substance abuse and sexual risk behavior profiles from our nationally representative sample of public high school students in the Dominican Republic. Using a novel methodological approach to control for selection bias, we examined explanations for the so-called Latino or Hispanic immigrant paradox. A latent class regression analysis with manifest and latent covariates found that US cultural involvement indicators were independent and robust predictors of increased risk of co-ocurring substance abuse and sexual risk behaviors. Implications for prevention efforts targeting risk behaviors among Latino/a adolescents in the US and abroad are considered.

On Causal Mediation Analysis With A Survival Outcome, Eric J. Tchetgen Tchetgen

Harvard University Biostatistics Working Paper Series

Suppose that having established a marginal total effect of a point exposure on a time-to-event outcome, an investigator wishes to decompose this effect into its direct and indirect pathways, also know as natural direct and indirect effects, mediated by a variable known to occur after the exposure and prior to the outcome. This paper proposes a theory of estimation of natural direct and indirect effects in two important semiparametric models for a failure time outcome. The underlying survival model for the marginal total effect and thus for the direct and indirect effects, can either be a marginal structural Cox proportional …

Semiparametric Estimation Of Models For Natural Direct And Indirect Effects, Eric J. Tchetgen Tchetgen, Ilya Shpitser

Harvard University Biostatistics Working Paper Series

In recent years, researchers in the health and social sciences have become increasingly interested in mediation analysis. Specifically, upon establishing a non-null total effect of an exposure, investigators routinely wish to make inferences about the direct (indirect) pathway of the effect of the exposure not through (through) a mediator variable that occurs subsequently to the exposure and prior to the outcome. Natural direct and indirect effects are of particular interest as they generally combine to produce the total effect of the exposure and therefore provide insight on the mechanism by which it operates to produce the outcome. A semiparametric theory …

Semiparametric Theory For Causal Mediation Analysis: Efficiency Bounds, Multiple Robustness, And Sensitivity Analysis, Eric J. Tchetgen Tchetgen, Ilya Shpitser

Harvard University Biostatistics Working Paper Series

Whilst estimation of the marginal (total) causal effect of a point exposure on an outcome is arguably the most common objective of experimental and observational studies in the health and social sciences, in recent years, investigators have also become increasingly interested in mediation analysis. Specifically, upon establishing a non-null total effect of the exposure, investigators routinely wish to make inferences about the direct (indirect) pathway of the effect of the exposure not through (through) a mediator variable that occurs subsequently to the exposure and prior to the outcome. Although powerful semiparametric methodologies have been developed to analyze observational studies, that …

Component Extraction Of Complex Biomedical Signal And Performance Analysis Based On Different Algorithm, Hemant Pasusangai Kasturiwale

Johns Hopkins University, Dept. of Biostatistics Working Papers

Biomedical signals can arise from one or many sources including heart ,brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The Biomedical time series signal are like electroencephalogram(EEG),electrocardiogram(ECG),etc The morphology of the cardiac signal is very important in most of diagnostics based on the ECG. The diagnosis of patient is based on visual observation of recorded ECG,EEG,etc, may not be accurate. To achieve better understanding , PCA (Principal Component Analysis) and ICA algorithms helps in analyzing ECG signals . The immense scope in the field of biomedical-signal processing Independent Component Analysis( …

Music And Radio Preferences On The Cal Poly Campus, Rory Bloch

Statistics

No abstract provided.

Using Survival Analysis Methods To Study Santa Barbara County Divorces, Joel Vazquez

Statistics

No abstract provided.

Dietary Patterns In Relation To Sleep And Stress In Cal Poly Freshman, Emily J. Conklin, Jongyoon Lee

Statistics

The Cal Poly FLASH study is a research project that was developed to assess overall health of college students. Beginning in Fall 2009, data have been collected longitudinally via online surveys and physical assessments on Cal Poly freshmen. Responses from 1520 students from Fall 2009 were used to investigate whether stress and sleeping habits are related to dietary patterns among Cal Poly students.

Factor analysis was used to categorize 33 food frequency variables into two categories – junk food and healthy food. Then, stepwise selection in a general linear model was conducted to identify lifestyle and demographic variables associated with …

An Exploration Of Non-Detects In Environmental Data, Juliana Fajardo

Statistics

No abstract provided.

Analysis Of Roms Estimated Posterior Error Utilizing 4dvar Data Assimilation, Joseph Patrick Horton

Mathematics

The appropriateness of the approximate error calculated by the Regional Ocean Modeling System (ROMS) is analyzed using Four-Dimensional Data Assimilation (4DVAR) performed on a numerical model of the San Luis Obispo Bay. An effective method of sampling data to minimize the actual error associated with the assimilated numerical model is explored by using different data sampling methods. An idealized state of the SLO bay region ("Real Run") is created to be used as the real ocean, then a numerical model of this region is created approximating this Real Run; this is known as the "Simulated State". By taking samples from …

An Extension Of Sic Predictions To The Wiener Coactive Model, Joseph W. Houpt, James T. Townsend

Psychology Faculty Publications

The survivor interaction contrasts (SIC) is a powerful measure for distinguishing among candidate models of human information processing. One class of models to which SIC analysis can apply are the coactive, or channel summation, models of human information processing. In general, parametric forms of coactive models assume that responses are made based on the first passage time across a fixed threshold of a sum of stochastic processes. Previous work has shown that the SIC for a coactive model based on the sum of Poisson processes has a distinctive down--up--down form, with an early negative region that is smaller than the …

Best Linear Unbiased Estimate Using Buys-Ballot Procedure When Trend-Cycle Component Is Linear, Ifeanyi S. Wueze, Nwogu C. Eleazar, Jude C. Ajaraogu

CBN Journal of Applied Statistics (JAS)

The Best linear unbiased estimate (BLUE) of Buys-Ballot estimates when trend-cycle component is linear are discussed in this paper. The estimates are those proposed by Iwueze and Nwogu (2004). Discussed are the Chain Based Estimation (CBE) method and the Fixed Based Estimation (FBE) method. The variates for the CBE method were found to have constant mean and variance but are correlated with only one significant autocorrelation coefficient at lag one. The variates for the FBE method were found to have constant mean, non-constant variance but with constant autocorrelation coefficient at all lags . Because the CBE variates exhibit stationarity, Best …

Stock Market Reaction To Selected Macroeconomic Variables In The Nigerian Economy, Abraham Williams Terfa

CBN Journal of Applied Statistics (JAS)

This study examines the relationship between the stock market and selected macroeconomic variables in Nigeria. The all share index was used as a proxy for the stock market while inflation, interest and exchange rates were the macroeconomic variables selected. Employing error correction model, it was found that a significant negative short run relationship exists between the stock market and the minimum rediscounting rate (MRR) implying that, a decrease in the MRR, would improve the performance of the Nigerian stock market. It was also found that exchange rate stability in the long run, improves the performance of the stock market. Though …

On Fractionally Integrated Logistic Smooth Transitions In Time Series, Olanrewaju I. Shittu, Yaya S. Olaolua

CBN Journal of Applied Statistics (JAS)

Long memory and nonlinearity are two key features of some macroeconomic time series which are characterized by persistent shocks that seem to rise faster during recession than it falls during expansion. A variant of nonlinear time series model together with long memory are used to examine these features in inflation series for three economies. The results which compares favourably with that of van Dijk et al. (2002) elicit some interesting attributes of inflation in the developed and developing economies.

Global Financial Meltdown And The Reforms In The Nigerian Banking Sector, Sanusi L. Sanusi

CBN Journal of Applied Statistics (JAS)

The paper examined the global financial meltdown and the reforms in the Nigerian banking sector. It was a public speech by the formal Governor of the Central Bank of Nigeria that observed the extent and severity of the crisis that began with the bursting of the housing bubble in the United States in August 2007 reflects the confluence of myriad of factors some of which are familiar from previous crises, while others are new. As in previous times of financial turmoil, the pre-crisis period was characterized by (i) surging asset prices that proved unsustainable; (ii) a prolonged credit expansion leading …