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Sampling Schemes For Policy Analyses Using Computer Simulation Experiments, Alicia L. Carriquiry, F. Jay Breidt, P. G. Lakshminarayan Nov 1997

Sampling Schemes For Policy Analyses Using Computer Simulation Experiments, Alicia L. Carriquiry, F. Jay Breidt, P. G. Lakshminarayan

CARD Working Papers

Evaluating the environmental and Economic impacts of agricultural policies is not a simple task. A systematic approach to evaluation would include the effect of policy-dependent factors (such as tillage practices, crop rotations, and chemical use) as well as the effect of policy independent covariates (such as weather, topography, and soil attributes) on response variables (such as amount of soil eroded or chemical leached into the groundwater). For comparison purposes, the effects of these input combinations on the response variable would have to be assessed under competing policy scenarios. Because the number of input combinations is high in most problems, and ...


Spatial Modeling Using Graphical Markov Models And Wavelets , Hsin-Cheng Huang Jan 1997

Spatial Modeling Using Graphical Markov Models And Wavelets , Hsin-Cheng Huang

Retrospective Theses and Dissertations

Graphical Markov models use graphs to represent possible dependencies among random variables. This class of models is extremely rich and includes inter alia causal Markov models and Markov random fields. In this dissertation, we develop a very efficient optimal-prediction algorithm for graphical Markov models. The algorithm is a generalization of the Kalman-filter algorithm for temporal processes, and it can in principle be applied to any Gaussian undirected graphical model and any Gaussian acyclic directed graphical model;We also propose a new class of multiscale models for stochastic processes in terms of scale-recursive dynamics defined on acyclic directed graphs. The models ...


A Comparison Of One-Sided Variables Acceptance Sampling Methods When Measurements Are Subject To Error , Peter Neville Morse Jan 1997

A Comparison Of One-Sided Variables Acceptance Sampling Methods When Measurements Are Subject To Error , Peter Neville Morse

Retrospective Theses and Dissertations

One-sided variables acceptance sampling plans such as the one presented in Schilling1 assume that a quality characteristic of interest, X, has a normal distribution and that measurements are exact. However, when measurements contain error and standard plans are used, the probability of accepting a lot for a fixed population proportion nonconforming varies widely depending on the population and measurement error parameter values. In this dissertation we consider methods for variables acceptance sampling in the presence of measurement error and evaluate their performance under lower bound constraints on the population variance;In the late 1950's, David, Fay and Walsh2 suggested ...


Estimation And Prediction For Non-Gaussian Autoregressive Processes , Pradipta Sarkar Jan 1997

Estimation And Prediction For Non-Gaussian Autoregressive Processes , Pradipta Sarkar

Retrospective Theses and Dissertations

In many real world situations there is no reason to believe that the time series observations are normally distributed. Therefore the estimation of the error distribution, estimation of the parameters of the process and prediction for non-Gaussian autoregressive time series models are of importance. In our development, it is assumed that there exists a transformation that transforms the error distribution to the normal distribution, and that this transformation can be represented by a regression spline function. The transformation, and hence, the error distribution is estimated by an estimation procedure based on the spline regression of the residual quantiles on the ...


Hierarchical Long-Memory Time Series Models , Nan-Jung Hsu Jan 1997

Hierarchical Long-Memory Time Series Models , Nan-Jung Hsu

Retrospective Theses and Dissertations

The long memory feature is well-known in many sciences, especially in physical sciences and increasingly in economics and finance. In this dissertation a new sampling-based Bayesian approach for fractionally integrated autoregressive moving average (ARFIMA) processes is proposed. In this method, a particular type of ARMA process is used as an approximation for the ARFIMA in a Metropolis-Hastings algorithm, and then importance sampling is used to adjust for the approximation error. This algorithm is relatively time-efficient because of fast convergence of the constructed Markov chain and fewer computations than competitors. Through a simulation study, the performance of the posterior means is ...


Several Techniques To Detect And Identify Systematic Biases When Process Constraints Are Bilinear , Shonda Roelfs Kuiper Jan 1997

Several Techniques To Detect And Identify Systematic Biases When Process Constraints Are Bilinear , Shonda Roelfs Kuiper

Retrospective Theses and Dissertations

This work develops and evaluates several new approaches that detect and identify biased measured variables when physical constraints (material and energy balances) are bilinear. The objective of each of these techniques is to develop [alpha]-level hypothesis tests for the detection and identification of measurement bias. Constraints are bilinear when two measured variables (each with a power of 1) are multiplied by one another. Bilinear Constraints are statistically more complex than linear constraints because the normality assumption is no longer valid. A study is presented to illustrate important strengths and weaknesses of each approach under a variety of process conditions ...


Nonlinear Measurement Error Analysis For System Monitoring , Jean Elizabeth Pelkey Jan 1997

Nonlinear Measurement Error Analysis For System Monitoring , Jean Elizabeth Pelkey

Retrospective Theses and Dissertations

In many physical and biological systems, underlying variables satisfy restrictions, but some or all of the variables are measured with error. The restrictions are often nonlinear in variables and may contain unknown parameters. Some of the restrictions may fail to hold at certain time points due to some system anomaly. While a good estimate of the measurement error covariance matrix is often available, systematic measurement biases may be present due to calibration or human errors. Statistical analysis of such systems is considered using a nonlinear errors-in-variables approach;The case considered first deals with a system in a stable condition, where ...


Inversion Of Sparse Matrices Using Monte Carlo Methods , Bassirou Chitou Jan 1997

Inversion Of Sparse Matrices Using Monte Carlo Methods , Bassirou Chitou

Retrospective Theses and Dissertations

A frequent need in many scientific applications is the flexibility to compute some suitable elements of the inverse of well-conditioned, large, sparse, and positive definite matrices. In this research, we have explored some aspects of the inversion of such matrices. For this class of matrices, it has been shown that desired elements of their inverse may be evaluated with desired accuracy via a statistical approach. In this approach, each element of the inverse matrix is decomposed as the sum of two components: a fixed quantity and an expectation of a well defined random variable. This approach works directly with the ...


New Approaches For Identification Of Systematic Measurement Errors In Linear Steady State And Dynamic Processes , Sriram Devanathan Jan 1997

New Approaches For Identification Of Systematic Measurement Errors In Linear Steady State And Dynamic Processes , Sriram Devanathan

Retrospective Theses and Dissertations

In this work three new methods are presented for improved identification of measurement biases in linear and nonlinear pseudo steady state processes. In addition to these methods, a new method is outlined for identification of biases in dynamic processes;The first method makes use of information contained in the relationship between individual measurements and the corresponding nodal balance. The performance of this method is demonstrated on a problem from the literature that has proved difficult for earlier methods. Additionally, this work discusses how the new technique can be used as a visual monitoring tool for identifying biased measured variables;The ...


Statistical Analysis Of Foreign Exchange Rates: Application Of Cointegration Model And Regime-Switching Stochastic Volatility Model , Koji Kondo Jan 1997

Statistical Analysis Of Foreign Exchange Rates: Application Of Cointegration Model And Regime-Switching Stochastic Volatility Model , Koji Kondo

Retrospective Theses and Dissertations

The dissertation discusses an application of two statistical models to foreign exchange rate data and consists of two main parts. The first part is an application of the partial cointegration model developed by Johansen (1990) and uses the concept of weak exogeneity. While a direct application of the cointegration approach with many variables is not easy to handle, the partial model can reduce the number of the parameters to be estimated by identifying weakly exogenous variables. The method is illustrated utilizing a theoretical long-run model based on Dornbusch's sticky price model. The small country assumption is relaxed to that ...


Econometric Analysis Of Measurement Error In Panel Data , Elizabeth Martha Paterno Jan 1997

Econometric Analysis Of Measurement Error In Panel Data , Elizabeth Martha Paterno

Retrospective Theses and Dissertations

Panel data consist of measurements taken from several individuals over time. Correlation among measurements taken from the same individual are often accounted for using random effect and random coefficient models. Panel data analysis that accounts for measurement error in the explanatory variables has not been thoroughly studied. This dissertation investigates statistical issues associated with two types of measurement error models for panel data;The first paper considers identification and estimation of a random effect model when some explanatory variables are measured with error. Here, individual heterogeneity is assumed to be manifested in intercepts that randomly differ across individuals. Identification of ...


Planning Fatigue Experiments And Analyzing Fatigue Data With The Random Fatigue-Limit Model And Modified Sudden Death Tests , Francis Garcia Pascual Jan 1997

Planning Fatigue Experiments And Analyzing Fatigue Data With The Random Fatigue-Limit Model And Modified Sudden Death Tests , Francis Garcia Pascual

Retrospective Theses and Dissertations

In this research, we address important issues faced by researchers in fatigue testing. We suggest a practical model to describe the relationship between fatigue life and applied stress, illustrate the corresponding data analysis methods, and study test plans under this model. We also present test plans that provide a systematic and efficient use of a limited number of test positions. These methods emphasize the importance of accuracy in the study of fatigue life while recognizing physical realities and resource limitations;In a fatigue-limit model, test units tested below the fatigue limit (also known as the threshold stress) theoretically will never ...


Specification Of Dependence Structures And Simulation-Based Estimation For Conditionally Specified Statistical Models , Jaehyung Lee Jan 1997

Specification Of Dependence Structures And Simulation-Based Estimation For Conditionally Specified Statistical Models , Jaehyung Lee

Retrospective Theses and Dissertations

Conditionally specified statistical models are frequently constructed from conditional one-parameter exponential family distributions. One way to formulate such a model is to specify the dependence structure among random variables through the use of a Markov random field. When this is done, a common assumption is that dependence is expressed only through pairs of random variables, the 'pairwise-only dependence' assumption. Using a Markov random field structure and the pairwise-only dependence assumption, Besag (1974) formulated exponential family 'auto-models', and showed the form that conditional one-parameter exponential family densities must have in such models. Those results are extended under relaxation of the pairwise-only ...


Consistent Estimation Using Approximate Likelihoods , Marek Brabec Jan 1997

Consistent Estimation Using Approximate Likelihoods , Marek Brabec

Retrospective Theses and Dissertations

The thesis consists of two papers. The first develops an estimation technique termed Adjusted Quasi Maximum Likelihood Estimation (AQMLE), while the second applies it to a particular situation of practical interest and compares its performance to several other methods of estimation in a simulation study. The AQMLE is a two-step procedure. It starts with a quasi maximum likelihood estimate (QMLE), obtained by maximization of an incorrect likelihood (quasilikelihood). The quasilikelihood is often a simplified approximation to the correct likelihood, used to avoid excessive numerical computations required to maximize the true likelihood. On the second step, the QMLE is adjusted to ...