@article {1971141,
	title = {The Return on R\&D Versus Capital Expenditures in the Pharmaceutical and Chemistry Industies},
	journal = {IEEE Transactions on Engineering Management},
	volume = {50},
	year = {2003},
	month = {2003},
	pages = {141 -150},
	abstract = {The impact of research and development (R\&D) on firm performance is generally agreed to be positive, but the nature and extent of this impact share little agreement in the previous research. Using an improved, time series, cross-sectional regression model that accounts for both contemporaneous and firm-specific serial correlation, as well as the feedback between firm profitability and investments, our study compares the rate of return from a dollar investment on R\&D to a dollar investment on fixed assets in pharmaceutical and chemical industries. We find positive associations of R\&D intensity and all variables of firm performance (net margin, operating margin, sales growth, and market value). We find that an investment in R\&D earns an operating margin return much higher than the industry cost of capital. We also find that the effect of an investment in R\&D on the firm{\textquoteright}s market value is about twice as much the effect of an investment in fixed assets. These findings have implications for corporate investment strategies, indicating that additional R\&D investment is more likely to provide a firm with a unique and sustainable competitive advantage.},
	keywords = {Strategy \& Entrepreneurship, Supply Chain},
	author = {Hsieh,Ping-Hung and Mishra,C. S. and Gobeli,Dave}
}
@article {1971976,
	title = {Robustness of Conditional Moments: An Application to Premium Calculation for Reinsurance Treaties},
	journal = {Risk Analysis},
	volume = {21},
	year = {2001},
	month = {2001},
	pages = {225-234},
	abstract = {In this study, the tail probability of a class of distributions commonly used in assessing the severity of insurance losses was examined. Without specifying any particular distribution, the use of an algebraic functional form Cx to approximate the tail behavior of the distributions in the class was demonstrated. Norwegian fire insurance data were examined, and the algebraic functional form was applied to derive the expected loss of a reinsurance treaty that covers all losses exceeding a retention limit. It was shown that (1) the expected loss is insensitive to the parameter {\'a} for a high retention limit (e.g., a catastrophe treaty), and (2) with a low retention limit (e.g., a largest claim treaty), a reliable estimate of the parameter {\'a} and a sound judgment on the maximum potential loss of the treaty could provide useful and defensible summary statistics for pricing the treaty. Thus, when dealing with the losses of certain reinsurance treaties, it was concluded that knowledge of a specific probability distribution is not critical, and the summary statistics derived from the model are robust with respect to a large class of loss distributions.},
	keywords = {Supply Chain},
	author = {Hsieh,Ping-Hung}
}
@article {1979291,
	title = {The Returns to R\&D and Capital Expenditures in the Pharmaceuticals and Chemicals Industry},
	year = {2000},
	month = {2000},
	address = {Seattle, Washington},
	keywords = {Supply Chain},
	author = {Hsieh,Ping-Hung and Mishra,Chandra  S. and Gobeli,David H.}
}
@article {1979296,
	title = {Robustness of Conditional Moments: An Application to Premium Calculation for Reinsurance Treaties},
	year = {1999},
	month = {1999},
	address = {Baltimore, Maryland},
	keywords = {Supply Chain},
	author = {Hsieh,Ping-Hung}
}
@article {1971981,
	title = {Robustness of Tail Index Estimation},
	journal = {Journal of Computational and Graphical Statistics},
	volume = {8},
	year = {1999},
	month = {1999},
	pages = {318-332},
	abstract = {The implementation of the Hill estimator, which estimates the heaviness of the tail of a distribution, requires a choice of the number of extreme observations in the tails, $r$, from a sample of size $n$, where $2 \leq r+1 \leq n$. This article is concerned with a robust procedure of choosing an optimal $r$. Thus, an estimation procedure, $\delta_s$, based on the idea of spacing statistics, $H^{(r)}$, is developed. The proposed decision rule for choosing $r$ under the squared error loss is found to be a simple function of the sample size. The proposed rule is then illustrated across a wide range of data, including insurance claims, currency exchange rate returns, and city size.},
	keywords = {Supply Chain},
	author = {Hsieh,Ping-Hung}
}
