Nucleon-nucleon scattering using dispersion relations

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DISTRIBUTION CHOICE UNDER NULL PRIORS AND SMALL SAMPLE SIZE

Defining appropriate probability distributions for the variables in an economic model is an important and often arduous task. This paper evaluates the performance of several common probability distributions under different distributional assumptions when sample sizes are small and there is limited information about the data.Research Methods/ Statistical Methods,

Stochastic Optimization: An Application to Sub-Arctic Dairy Farming

The paper demonstrates how a deterministic farm linear programming (LP) model can be made stochastic and simulated using Solver and Simetar© in Excel©. The demonstration is conducted with an LP-model for a dairy farm for a sub arctic region of Norway. The income risks arising from variation in milk and crop yields due to winter damage in leys and pastures have been quantified for farms demonstrating low, medium and high forage yield risk. The estimated distribution of farm profit will be skewed to the left, indicating a downside risk. In the presence of risks, farmers maximize income by producing the milk quota with using surplus forage for meat production. The analysis demonstrated here may assist farmers and farm managers in improving sensitivity analysis for risky variables in farm LP models.dairy production, Northern Norway, stochastic optimization, stochastic simulation, yield risks, Livestock Production/Industries,

Comparison of Alternative Safety Net Programs for the 2000 Farm Bill

As Congress begins its debate for the 2002 farm bill, there have been calls for a counter cyclical safety net that will provide a better basis for targeting longer term planning than exists with ad hoc emergency assistance. Further subsidization of the multi-peril crop insurance (MPCI) program has been proposed, as well as reliance on a farm and ranch risk management (FARRM) account to help farmers. A whole farm revenue income support program and several variations of national income supplement programs have been put forward. A comprehensive analysis of different safety net alternatives using a common methodology is needed so farmers and policy makers can make objective comparisons. The objective of this paper is to quantitatively analyze the economic effects of alternative safety net/insurance programs on farmers in the Southern United States. The objective is accomplished by simulating representative crop farms in the South over the 2001-2005 planning horizon for alternative safety net options. The simulated net present value distributions for the farms are compared using certainty equivalents to determine the value of alternative safety net options to feed grain, cotton and rice farms in the South.Agricultural and Food Policy,

Genomic Education – Bench to Bedside: A Novel Approach to Teaching Genetic Diagnosis

Problem: Teaching genetic diagnosis is required in all medical schools and physician assistant programs. However, with thousands of relevant findings and thousands more rare diseases, lectures and narrative resources are inadequate for the task. Whatever information that is taught is easily forgotten and does not carry over into the clinic. Many rare disease patients suffer through “diagnostic odysseys” (3 to 30 years to correct diagnosis). Approach: We used a commercially available diagnostic decision support system (DDSS) that encompasses all Mendelian disorders with known genes, together with other conditions in their differential diagnosis, and a case-based educational approach to teach diagnostic skills in a way that could then be replicated in the clinic. After a lecture, which included a demonstration using the DDSS with a sample case, 74 students were assigned to replicate the sample case at home and then complete 7 other anonymized cases, all with known rare diagnoses. After each case, students saved the “patient summary” that included the findings entered and differential diagnosis list and submitted it as homework. Students also completed a questionnaire about their experience, including satisfaction. Outcomes: Students were effective at diagnosing rare diseases in 483 of the 514 testing instances, a 94% success rate, with success defined as the correct diagnosis being listed in the differential diagnosis. Eighty-five percent of students rated this interactive learning session “highly,” encouraged us to repeat the assignment next year, and 89% reported that they wanted to use the DDSS during their clinical rotations in the coming year. Next Steps: We plan to refine the cases, add more material on findings, and ensure that all the synonyms students might use are in the software tool. We plan to repeat the program next year and recommend its use more widely in medical education

RCT Rejection Sampling for Causal Estimation Evaluation

Confounding is a significant obstacle to unbiased estimation of causal effects from observational data. For settings with high-dimensional covariates -- such as text data, genomics, or the behavioral social sciences -- researchers have proposed methods to adjust for confounding by adapting machine learning methods to the goal of causal estimation. However, empirical evaluation of these adjustment methods has been challenging and limited. In this work, we build on a promising empirical evaluation strategy that simplifies evaluation design and uses real data: subsampling randomized controlled trials (RCTs) to create confounded observational datasets while using the average causal effects from the RCTs as ground-truth. We contribute a new sampling algorithm, which we call RCT rejection sampling, and provide theoretical guarantees that causal identification holds in the observational data to allow for valid comparisons to the ground-truth RCT. Using synthetic data, we show our algorithm indeed results in low bias when oracle estimators are evaluated on the confounded samples, which is not always the case for a previously proposed algorithm. In addition to this identification result, we highlight several finite data considerations for evaluation designers who plan to use RCT rejection sampling on their own datasets. As a proof of concept, we implement an example evaluation pipeline and walk through these finite data considerations with a novel, real-world RCT -- which we release publicly -- consisting of approximately 70k observations and text data as high-dimensional covariates. Together, these contributions build towards a broader agenda of improved empirical evaluation for causal estimation.Comment: Code and data at https://github.com/kakeith/rct_rejection_samplin

Representative Farm Analysis of the H.R. 2646 Farm Bill Proposal

Agricultural and Food Policy,

Economic Outlook for Representative Cotton Farms Given the August 2003 FAPRI/AFPC Baseline

The farm level economic impacts of the Farm Security and Rural Investment Act of 2002 on representative cotton farms are projected in this report. The analysis was conducted over the 2001-2007 planning horizon using FLIPSIM, AFPC’s whole farm simulation model. Data to simulate farming operations in the nation’s major cotton production regions came from two sources: - Producer panel cooperation to develop economic information to describe and simulate representative cotton farms. - Projected prices, policy variables, and input inflation rates from the Food and Agricultural Policy Research Institute (FAPRI) August 2003 Baseline. The primary objective of the analysis is to determine the farms’ economic viability by region through the life of the 2002 Farm Bill, given sector level conditions projected in the August 2003 FAPRI Baseline. The FLIPSIM policy simulation model incorporates the historical risk faced by cotton farmers for prices and production. This report presents the results of the August 2003 Baseline in a risk context using selected simulated probabilities and ranges for annual net cash farm income values. The probability of a farm experiencing annual cash flow deficits and the probability of a farm losing real net worth are included as indicators of the cash flow and equity risks facing farms through the year 2007. This report is organized into five sections. The first section summarizes the process used to develop the representative farms and the key assumptions utilized for the farm level analysis. The second section summarizes the FAPRI August 2003 Baseline and the policy and price assumptions used for the representative farm analyses. The third section presents the results of the simulation analyses for cotton farms. Two appendices constitute the final section of the report. Appendix A provides tables to summarize the physical and financial characteristics for each of the representative farms. Appendix B provides the names of producers, land grant faculty, and industry leaders who cooperated in the panel interview process to develop the representative cotton farms.Agribusiness, Agricultural and Food Policy, Crop Production/Industries,

Representative Farms Economic Outlook for the January 2003 FAPRI/AFPC Baseline

The farm level economic impacts of the Farm Security and Rural Investment Act of 2002 on representative crop and livestock operations are projected in this report. The analysis was conducted over the 2001-2007 planning horizon using FLIPSIM, AFPC’s whole farm simulation model. Data to simulate farming operations in the nation’s major production regions came from two sources: - Producer panel cooperation to develop economic information to describe and simulate representative crop, livestock, and dairy farms. - Projected prices, policy variables, and input inflation rates from the Food and Agricultural Policy Research Institute (FAPRI) January 2003 Baseline. The FLIPSIM policy simulation model incorporates the historical risk faced by farmers for prices and production. This report presents the results of the January 2003 Baseline in a risk context using selected simulated probabilities and ranges for annual net cash farm income values. The probability of a farm experiencing annual cash flow deficits and the probability of a farm losing real net worth are included as indicators of the cash flow and equity risks facing farms through the year 2007.Agribusiness, Agricultural and Food Policy, Crop Production/Industries,