A General Instrumental Variable Framework For Regression Analysis With Outcome Missing Not At Random,
2013
Harvard University
A General Instrumental Variable Framework For Regression Analysis With Outcome Missing Not At Random, Eric J. Tchetgen Tchetgen, Kathleen Wirth
Harvard University Biostatistics Working Paper Series
No abstract provided.
Alternative Identification And Inference For The Effect Of Treatment On The Treated With An Instrumental Variable,
2013
Harvard University
Alternative Identification And Inference For The Effect Of Treatment On The Treated With An Instrumental Variable, Eric J. Tchetgen Tchetgen, Stijn Vansteelandt
Harvard University Biostatistics Working Paper Series
No abstract provided.
Identification And Estimation Of Survivor Average Causal Effects,
2013
Harvard University
Identification And Estimation Of Survivor Average Causal Effects, Eric J. Tchetgen Tchetgen
Harvard University Biostatistics Working Paper Series
No abstract provided.
Practical Guidelines For The Comprehensive Analysis Of
Chip-Seq Data,
2013
The University of Queensland
Practical Guidelines For The Comprehensive Analysis Of Chip-Seq Data, Timonthy Bailey, Pawel Krajewski, Istvan Ladunga, Celine Lefebvre, Qunhua Li, Tao Liu, Pedro Madrigal, Cenny Taslim, Jie Zhang
Department of Statistics: Faculty Publications
Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality …
On The Causal Interpretation Of Race In Regressions Adjusting For Confounding And Mediating Variables,
2013
Harvard University
On The Causal Interpretation Of Race In Regressions Adjusting For Confounding And Mediating Variables, Tyler J. Vanderweele, Whitney Robinson
Harvard University Biostatistics Working Paper Series
We consider different possible interpretations of the “effect of race” when regressions are run with race as an exposure variable, controlling also for various confounding and mediating variables. When adjustment is made for socioeconomic status early in a person's life, we discuss under what contexts the regression coefficients for race can be interpreted as corresponding to the extent to which a racial disparity would remain if various socioeconomic distributions early in life across racial groups could be equalized. When adjustment is also made for adult socioeconomic status, we note how the overall disparity can be decomposed into the portion that …
A Unification Of Mediation And Interaction,
2013
Harvard University
A Unification Of Mediation And Interaction, Tyler J. Vanderweele
Harvard University Biostatistics Working Paper Series
We show that the overall effect of an exposure on an outcome, in the presence of a mediator with which the exposure may interact, can be decomposed into four components: (i) the effect of the exposure in the absence of the mediator, (ii) the interactive effect when the mediator is left to what is would be in the absence of exposure, (iii) a mediated interaction and (iv) a pure mediated effect. These four components respectively correspond to the portion of the effect that is due to neither mediation nor interaction, to just interaction (but not mediation), to both mediation and …
Joint Estimation Of Multiple Graphical Models From High Dimensional Time Series,
2013
Johns Hopkins University
Joint Estimation Of Multiple Graphical Models From High Dimensional Time Series, Huitong Qiu, Fang Han, Han Liu, Brian Caffo
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this manuscript the problem of jointly estimating multiple graphical models in high dimensions is considered. It is assumed that the data are collected from n subjects, each of which consists of m non-independent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of the closeness between subjects. A kernel based method for jointly estimating all graphical models is proposed. Theoretically, under a double asymptotic framework, where both (m,n) and the dimension d can increase, the explicit rate of convergence in parameter estimation is provided, thus characterizing the strength one can borrow …
A Monte Carlo Comparison Of Robust Manova Test Statistics,
2013
Ball State University, Muncie, IN
A Monte Carlo Comparison Of Robust Manova Test Statistics, Holmes Finch, Brian French
Journal of Modern Applied Statistical Methods
Multivariate Analysis of Variance (MANOVA) is a popular statistical tool in the social sciences, allowing for the comparison of mean vectors across groups. MANOVA rests on three primary assumptions regarding the population: (a) multivariate normality, (b) equality of group population covariance matrices and (c) independence of errors. When these assumptions are violated, MANOVA does not perform well with respect to Type I error and power. There are several alternative test statistics that can be considered including robust statistics and the use of the structural equation modeling (SEM) framework. This simulation study focused on comparing the performance of the P test …
On Some Properties Of A Heterogeneous Transfer Function Involving Symmetric Saturated Linear (Satlins) With Hyperbolic Tangent (Tanh) Transfer Functions,
2013
University of Ibadan, Ibadan, Nigeria
On Some Properties Of A Heterogeneous Transfer Function Involving Symmetric Saturated Linear (Satlins) With Hyperbolic Tangent (Tanh) Transfer Functions, Christopher Godwin Udomboso
Journal of Modern Applied Statistical Methods
For transfer functions to map the input layer of the statistical neural network model to the output layer perfectly, they must lie within bounds that characterize probability distributions. The heterogeneous transfer function, SATLINS_TANH, is established as a Probability Distribution Function (p.d.f), and its mean and variance are shown.
Distribution Of The Ratio Of Normal And Rice Random Variables,
2013
University of Isfahan, Isfahan, Iran
Distribution Of The Ratio Of Normal And Rice Random Variables, Nayereh B. Khoolenjani, Kavoos Khorshidian
Journal of Modern Applied Statistical Methods
The ratio of independent random variables arises in many applied problems. The distribution of the ratio |X/Y| is studied when X and Y are independent Normal and Rice random variables, respectively. Ratios of such random variables have extensive applications in the analysis of noises in communication systems. The exact forms of probability density function (PDF), cumulative distribution function (CDF) and the existing moments are derived in terms of several special functions. As a special case, the PDF and CDF of the ratio of independent standard Normal and Rayleigh random variables have been obtained. Tabulations of associated percentage points …
Front Matter,
2013
Wayne State University
Front Matter, Jmasm Editors
Journal of Modern Applied Statistical Methods
No abstract provided.
Preliminary Testing For Normality: Is This A Good Practice?,
2013
University of Manitoba, Winnipeg, Manitoba
Preliminary Testing For Normality: Is This A Good Practice?, H. J. Keselman, Abdul R. Othman, Rand R. Wilcox
Journal of Modern Applied Statistical Methods
Normality is a distributional requirement of classical test statistics. In order for the test statistic to provide valid results leading to sound and reliable conclusions this requirement must be satisfied. In the not too distant past, it was claimed that violations of normality would not likely jeopardize scientific findings (See Hsu & Feldt, 1969; Lunney, 1970). Recent revelations suggest otherwise (See e.g., Micceri, 1989; Keselman, Huberty, Lix et al., 1998; Erceg-Hurn, Wilcox, & Keselman, 2013; Wilcox and Keselman, 2003; Wilcox, 2012a, b). Unfortunately the data obtained in psychological investigations rarely, if ever, meet the requirement of normally distributed data (Micceri, …
Intrinsically Ties Adjusted Non-Parametric Method For The Analysis Of Two Sampled Data,
2013
Nnamdi Azikiwe University, Awka, Nigeria
Intrinsically Ties Adjusted Non-Parametric Method For The Analysis Of Two Sampled Data, G. U. Ebuh, I. C. A Oyeka
Journal of Modern Applied Statistical Methods
A non-parametric method for the analysis of two sample data is proposed that intrinsically and structurally adjusts the test statistic for the possible presence of tied observations between the sampled populations, thereby obviating the need to require the populations to be continuous. The populations may be measurements on as low as the ordinal scale, and need not be homogeneous. In cases where the null hypotheses are rejected, the test statistic enables the determination of which of the sampled populations is likely to be responsible for the rejection (a determination which the Wilcoxon Mann Whitney test cannot handle). The proposed method …
The Impact Of Continuity Violation On Anova And Alternative Methods,
2013
Chalmers University of Technology, Gothenburg, Sweden
The Impact Of Continuity Violation On Anova And Alternative Methods, Björn Lantz
Journal of Modern Applied Statistical Methods
The normality assumption behind ANOVA and other parametric methods implies that response variables are measured on continuous scales. A simulation approach is used to explore the impact of continuity violation on the performance of statistical methods commonly used by applied researchers to compare locations across several groups.
The Single-Case Data Analysis Package: Analysing Single-Case Experiments With R Software,
2013
KU Leuven, Belgium
The Single-Case Data Analysis Package: Analysing Single-Case Experiments With R Software, Isis Bulté, Patrick Onghena
Journal of Modern Applied Statistical Methods
The RcmdrPlugin.SCDA plug-in package is discussed. It integrates three R packages in the R commander interface: SCVA (for Single-Case Visual Analysis), SCRT (for Single-Case Randomization Tests), and SCMA (for Single-Case Meta-Analysis). This way the plug-in package covers three important steps in the analysis of single-case data.
A Comparison Between Biased And Unbiased Estimators In Ordinary Least Squares Regression,
2013
King Khalid University, Saudi Arabia
A Comparison Between Biased And Unbiased Estimators In Ordinary Least Squares Regression, Ghadban Khalaf
Journal of Modern Applied Statistical Methods
During the past years, different kinds of estimators have been proposed as alternatives to the Ordinary Least Squares (OLS) estimator for the estimation of the regression coefficients in the presence of multicollinearity. In the general linear regression model, Y = Xβ + e, it is known that multicollinearity makes statistical inference difficult and may even seriously distort the inference. Ridge regression, as viewed here, defines a class of estimators of β indexed by a scalar parameter k. Two methods of specifying k are proposed and evaluated in terms of Mean Square Error (MSE) by …
Discriminating Between Generalized Exponential Distribution And Some Life Test Models Based On Population Quantiles,
2013
R. V. R. and J. C. College of Engineering, Guntur, India
Discriminating Between Generalized Exponential Distribution And Some Life Test Models Based On Population Quantiles, B. Srinivasa Rao, R. R. L Kantam
Journal of Modern Applied Statistical Methods
A test statistic based on population quantiles using sample order statistics is suggested. The quantiles of the test statistics are evaluated for generalized exponential distribution. Similar test statistic based on moments of sample order statistic is referred and the proposed test formula is compared with it. Between the pairs of the above models it is established that the test formula proposed by us is more effective and useful than the formula based on the moments of order statistics as developed by Sultan (2007).
Comparison Of Parameters Of Lognormal Distribution Based On The Classical And Posterior Estimates,
2013
University of Kashmir, Srinagar, India
Comparison Of Parameters Of Lognormal Distribution Based On The Classical And Posterior Estimates, Raja Sultan, S. P. Ahmad
Journal of Modern Applied Statistical Methods
Lognormal distribution is widely used in scientific field, such as agricultural, entomological, biology etc. If a variable can be thought as the multiplicative product of some positive independent random variables, then it could be modelled as lognormal. In this study, maximum likelihood estimates and posterior estimates of the parameters of lognormal distribution are obtained and using these estimates we calculate the point estimates of mean and variance for making comparisons.
On Bayesian Estimation And Predictions For Two-Component Mixture Of The Gompertz Distribution,
2013
Allama Iqbal Open University, Islamabad, Pakistan
On Bayesian Estimation And Predictions For Two-Component Mixture Of The Gompertz Distribution, Navid Feroze, Muhammad Aslam
Journal of Modern Applied Statistical Methods
Mixtures models have received sizeable attention from analysts in the recent years. Some work on Bayesian estimation of the parameters of mixture models have appeared. However, the were restricted to the Bayes point estimation The methodology for the Bayesian interval estimation of the parameters for said models is still to be explored. This paper proposes the posterior interval estimation (along with point estimation) for the parameters of a two-component mixture of the Gompertz distribution. The posterior predictive intervals are also derived and evaluated. Different informative and non-informative priors are assumed under a couple of loss functions for the posterior analysis. …
A Generalized Class Of Estimators For Finite Population Variance In Presence Of Measurement Errors,
2013
Banaras Hindu University, Varanasi, India
A Generalized Class Of Estimators For Finite Population Variance In Presence Of Measurement Errors, Prayas Sharma, Rajesh Singh
Journal of Modern Applied Statistical Methods
The problem of estimating the population variance is presented using auxiliary information in the presence of measurement errors. The estimators in this article use auxiliary information to improve efficiency and assume that measurement error is present both in study and auxiliary variable. A numerical study is carried out to compare the performance of the proposed estimator with other estimators and the variance per unit estimator in the presence of measurement errors.
