## Type of Work

Article (301) Book (0) Theses (50) Multimedia (0)

## Peer Review

Peer-reviewed only (300)

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## Publication Year

## Campus

UC Berkeley (55) UC Davis (35) UC Irvine (25) UCLA (26) UC Merced (16) UC Riverside (34) UC San Diego (43) UCSF (19) UC Santa Barbara (24) UC Santa Cruz (35) UC Office of the President (48) Lawrence Berkeley National Laboratory (164) UC Agriculture & Natural Resources (0)

## Department

Research Grants Program Office (RGPO) (48) University of California Research Initiatives (UCRI) (2)

University of California Transportation Center (3) Bourns College of Engineering (2) Center for Environmental Design Research (2) Center for the Built Environment (2)

## Journal

Dermatology Online Journal (18) International Organization of Citrus Virologists Conference Proceedings (1957-2010) (2) Electronic Green Journal (1) Western Journal of Emergency Medicine: Integrating Emergency Care with Population Health (1)

## Discipline

Engineering (7) Architecture (3) Life Sciences (3) Medicine and Health Sciences (2) Physical Sciences and Mathematics (2) Social and Behavioral Sciences (2) Arts and Humanities (1)

## Reuse License

BY - Attribution required (15) BY-NC-ND - Attribution; NonCommercial use; No derivatives (12) BY-NC-SA - Attribution; NonCommercial use; Derivatives use same license (1) BY-ND - Attribution; No derivatives (1) BY-SA - Attribution; Derivatives must use same license (1)

## Scholarly Works (352 results)

The valuation of travel time savings has been an important theme in transportation research because it is the single largest contributor to the benefits of many transportation projects. It also plays a central role in deciding about the size and scope of public investment and has important welfare implications. It can shed important light as to whether any congestion pricing scheme will have increase social welfare or not. And help us understand how commuters make their travel decisions. The San Diego I-15 Congestion Pricing Project (SDCPP) is a demonstration project where an existing High Occupancy Vehicle lane has converted to HOT (High Occupancy/Toll) lane. Beginning in 1996 these lanes were made available to solo drivers who pay a toll. The toll adjusts every six minutes to maintain free flowing traffic on the HOT lane. Carpoolers get to use the lane for free as before. This presents us with a unique opportunity to study commutersâ€™ choice between a tolled and a free alternative based on not only what the commuters say they would do (SP), but also on what they actually did (RP).

The general result is that this tolled facility is used by high income, middle aged, homeowners, female commuters. An interesting result that comes out of this analysis is the dual effects of toll. If the actual toll rises above the mean toll then the commuter is more likely to take the FasTrak lane. Another interesting implication that the effect of toll is conditional on the level of uncertainty of travel time and conversely uncertainty in travel time encourages use of FasTrak lane only if toll rises above a threshold value. Commuters are more sensitive to variations in travel time in the morning, specially during the peak, than in the afternoon.

Another salient result is that the Value of Time estimates from Stated Preference models are significantly lower than the Revealed Preference estimates. The difference is consistent and persistent across the different models which lead to the conclusion that these differences are real. Probably it reflects the difference in responses of individuals to actual and hypothetical situations.

We investigate the estimation issues for count data in dose response model. In

this thesis, we are considering logistic dose response model for a mixture experiment

with two drugs. We propose two new methods of estimation of parameters for this

model by forming the observation pairs. The standard maximum likelihood estima-

tion method uses the numerical methods for solving the estimating equations. This

method requires an initial set of values for the parameters in the model. The standard

procedure normally uses the initial values as zero or some convenient numbers without

any justication. We present two very systematic methods of nding the initial values

of parameters of the maximum likelihood estimating equations (MLEE). Our methods

are based on two criterion functions, the log-likelihood and the other function . We

then use the initial values and the corresponding criterion function to obtain the nal

solution of MLEE. We demonstrate that when we consider only two doses from the

data, we do have an exact analytic expression for the solution of estimating equations.

We use that fact to obtain the initial values of parameters in these models. Then we

have used the search algorithm for performing the optimization to nd the nal esti-

mates. The proposed methods are transparent in the selection of the initial values of

parameters. The proposed methods are computer intensive like bootstrap and jack-

knife methods popular among statisticians. We have also compared our estimates with

the estimates obtained by SAS and R. The proposed methods compare favorably with SAS and R in terms numerical values of the estimates and the performance time of the

estimates. We illustrate our methods with a data set (Giltinan, 1998). We present also

some simulated data to illustrate our methods.

- 1 supplemental PDF

The normal distribution is symmetric and enjoys many important properties. That is why it is widely used in practice. Asymmetry in data is a situation where the normality assumption is not valid. Azzalini (1985) introduces the skew normal distribution reflecting varying degrees of skewness. The skew normal distribution is mathematically tractable and includes the normal distribution as a special case. It has three parameters: location, scale and shape. In this thesis we attempt to respond to the complexity and challenges in the maximum likelihood estimates of the three parameters of the skew normal distribution. The complexity is traced to the ratio of the normal density and distribution function in the likelihood equations in the presence of the skewness parameter. Solution to this problem is obtained by approximating this ratio by linear and non-linear functions. We observe that the linear approximation performs quite satisfactorily. In this thesis, we present a method of estimation of the parameters of the skew normal distribution based on this linear approximation. We define a performance measure to evaluate our approximation and estimation method based on it. We present the simulation studies to illustrate the methods and evaluate their performances.

Several methods are available in literature for estimating the variance components in mixed effects models. In this thesis we consider the general mixed effects model without making any distributional assumptions. The quadratic unbiased estimators are considered for estimating the variance components. The uniformly minimum variance quadratic unbiased estimation (UMVQUE) of variance components is investigated for the data obtained from both balanced and unbalanced designs. In spite of its attractive properties, the UMVQUE may not always be possible. When the UMVQUE is not possible, we propose two alternative methods for estimating the variance components. We first introduce a method of near uniformly minimum variance quadratic unbiased estimation (NUMVQUE) for an unbalanced incomplete block design. When the UMVQUE of variance components is not possible for a design with replicated blocks but it is possible with a single replication of blocks, we propose another method of average uniformly minimum variance quadratic unbiased estimation (AUMVQUE). The maximum likelihood estimation (MLE) and restricted maximum likelihood estimation (REMLE) are likelihood based procedures and therefore require the distributional assumptions to estimate the variance components. We present a simulation study to evaluate the performance of our proposed estimation methods and compare them with MLE and REMLE.