Nonparametric location estimators in the randomized complete block design

Several tests for the comparison of different groups in the randomized complete block design exist. However, there is a lack of robust estimators for the location difference between one group and all the others on the original scale. The relative marginal effects are commonly used in this situation, but they are more difficult to interpret and use by less experienced people because of the different scale. In this paper two nonparametric estimators for the comparison of one group against the others in the randomized complete block design will be presented. Theoretical results such as asymptotic normality, consistency, translation invariance, scale preservation, unbiasedness, and median unbiasedness are derived. The finite sample behavior of these estimators is derived by simulations of different scenarios. In addition, possible confidence intervals with these estimators are discussed and their behavior derived also by simulations.

AN INVESTIGATION INTO THE EFFECT OF TESTOSTERONE ON THE QUANTITATIVE MAINTENANCE OF SPERMATOGENESIS IN THE HYPOPHYSECTOMIZED ADULT RAT (PHARMACOKINETICS, MORPHOMETRY, STATISTICS)

Administration of gonadotropins or testosterone (T) will maintain qualitatively normal spermatogenesis and fertility in hypophysectomized (APX) rats. However, quantitative maintenance of the spermatogenic process in APX rats treated with T alone or in combination with follicle stimulating hormone (FSH) has not been demonstrated. Studies reported here were conducted to determine whether it would be possible to increase intratesticular testosterone (ITT) levels in APX rats to those found in normal animals by administration of appropriate amounts of testosterone propionate (TP) and if under these conditions spermatogenesis can be maintained quantitatively. Quantitative analysis of spermatogenesis was performed on stages VI and VII of the spermatogenic cycle utilizing criteria of Leblond and Clermont (1952) all cell types were enumerated. In a series of experiments designed to investigate the effects of T on spermatogenesis, TP was administered to 60 day old APX rats twice daily for 30 days in doses ranging from 0.6 to 15 mg/day or from 0.6 to 6.0 mg/day in combination with FSH. The results of this study demonstrate that the efficiency of transformation of type A to type B spermatogonia and the efficacy of the meiotic prophase are related to ITT levels, and that quantitatively normal completion of the reduction division requires normal ITT levels. The ratio of spermatids to spermatocytes in the vehicle-treated APX rats was 1:1.38; in the APX rats treated with 15 mg of TP it was 1:4.0 (the theoretically expected number). This study is probably the first to demonstrate: (1) the pharmacokinetics of TP, (2) the profile and quantity of T-immunoactivity in both serum and testicular tissue of APX and IC rats as well as APX rats treated with TP alone or in combination with FSH, (3) the direct correlation of serum T and ITT levels in treated APX rats (r = 0.9, p < 0.001) as well as in the IC rats (r = 0.9, p < 0.001), (4) the significant increase in the number of Type B spermatogonia, preleptotene and pachytene spermatocytes and round spermatids in TP-treated APX rats, (5) the correlation of the number of round spermatids formed in IC rats to ITT levels (r = 0.9, p < 0.001), and (6) the correlation of the quantitative maintenance of spermatogenesis with ITT levels (r = 0.7, p < 0.001) in the testes of TP-treated APX rats. These results provide direct experimental evidence for the key role of T in the spermatogenic process. ^

Some Identification Issues in Nonparametric Linear Models with Endogenous Regressors

In applied work economists often seek to relate a given response variable y to some causal parameter mu* associated with it. This parameter usually represents a summarization based on some explanatory variables of the distribution of y, such as a regression function, and treating it as a conditional expectation is central to its identification and estimation. However, the interpretation of mu* as a conditional expectation breaks down if some or all of the explanatory variables are endogenous. This is not a problem when mu* is modelled as a parametric function of explanatory variables because it is well known how instrumental variables techniques can be used to identify and estimate mu*. In contrast, handling endogenous regressors in nonparametric models, where mu* is regarded as fully unknown, presents di±cult theoretical and practical challenges. In this paper we consider an endogenous nonparametric model based on a conditional moment restriction. We investigate identification related properties of this model when the unknown function mu* belongs to a linear space. We also investigate underidentification of mu* along with the identification of its linear functionals. Several examples are provided in order to develop intuition about identification and estimation for endogenous nonparametric regression and related models.

Connecticut Poison Control Center. 2002 and 2003 Annual Report and Poison Exposure Statistics

The Connecticut Poison Control Center (CPCC) at the University of Connecticut Health Center (UCHC) was established in 1957 under Connecticut General Statute 10a- 132. The CPCC’s main responsibility is to provide 24-hour emergency toxicology management consultations for victims of poisoning, and serve as a source for pharmacology and toxicology-related information. The center monitors the epidemiology of human poisoning and provides surveillance for environmental and occupational chemical exposures, drug abuse, and pharmaceutical interactions and adverse effects. The CPCC performs toxicological research, and provides formal toxicology instruction for allied health professionals, as well as professional and consumer poison prevention education. The CPCC is one of 63 nationwide centers certified by the American Association of Poison Control Centers (AAPCC), and the only poison center in the state of Connecticut. The AAPCC establishes standards of care for poisoning and administers the Toxic Exposure Surveillance System (TESS), a national database of poisoning statistics, to which the CPCC is a contributor.

Direct and Indirect Measures of Capacity Utilization: A Nonparametric Analysis of U.S. Manufacturing

We measure the capacity output of a firm as the maximum amount producible by a firm given a specific quantity of the quasi-fixed input and an overall expenditure constraint for its choice of variable inputs. We compute this indirect capacity utilization measure for the total manufacturing sector in the US as well as for a number of disaggregated industries, for the period 1970-2001. We find considerable variation in capacity utilization rates both across industries and over years within industries. Our results suggest that the expenditure constraint was binding, especially in periods of high interest rates.

Efficiency Bounds for Estimating Linear Functionals of Nonparametric Regression Models with Endogenous Regressors

Consider a nonparametric regression model Y=mu*(X) + e, where the explanatory variables X are endogenous and e satisfies the conditional moment restriction E[e|W]=0 w.p.1 for instrumental variables W. It is well known that in these models the structural parameter mu* is 'ill-posed' in the sense that the function mapping the data to mu* is not continuous. In this paper, we derive the efficiency bounds for estimating linear functionals E[p(X)mu*(X)] and int_{supp(X)}p(x)mu*(x)dx, where p is a known weight function and supp(X) the support of X, without assuming mu* to be well-posed or even identified.

Liberalization, Ownership, and Efficiency in Indian Banking: A Nonparametric Approach

This paper empirically estimates and analyzes various efficiency scores of Indian banks during 1997-2003 using data envelopment analysis (DEA). During the 1990s India's financial sector underwent a process of gradual liberalization aimed at strengthening and improving the operational efficiency of the financial system. It is observed, none the less, that Indian banks are still not much differentiated in terms of input or output oriented technical efficiency and cost efficiency. However, they differ sharply in respect of revenue and profit efficiencies. The results provide interesting insight into the empirical correlates of efficiency scores of Indian banks. Bank size, ownership, and the fact of its being listed on the stock exchange are some of the factors that are found to have positive impact on the average profit efficiency and to some extent revenue efficiency scores are. Finally, we observe that the median efficiency scores of Indian banks in general and of bigger banks in particular have improved considerably during the post-reform period.

Are Indian Firms too Small? A Nonparametric Analysis of Cost Efficiency and Industry Structure of Indian Manufacturing

In this paper we use the 2004-05 Annual Survey of Industries data to estimate the levels of cost efficiency of Indian manufacturing firms in the various states and also get state level measures of industrial organization (IO) efficiency. The empirical results show the presence of considerable cost inefficiency in a majority of the states. Further, we also find that, on average, Indian firms are too small. Consolidating them to attain the optimal scale would further enhance efficiency and lower average cost.

Technical Efficiency in the Indian Textiles Industry: A Nonparametric Analysis of Firm-Level Data

The Indian textiles industry is now at the crossroads with the phasing out of quota regime that prevailed under the Multi-Fiber Agreement (MFA) until the end of 2004. In the face of a full integration of the textiles sector in the WTO, maintaining and enhancing productive efficiency is a precondition for competitiveness of the Indian firms in the new liberalized world market. In this paper we use data obtained from the Annual Survey of Industries for a number of years to measure the levels of technical efficiency in the Indian textiles industry at the firm level. We use both a grand frontier applicable to all firms and a group frontier specific to firms from any individual state, ownership, or organization type in order to evaluate their efficiencies. This permits us to separately identify how locational, proprietary, and organizational characteristics of a firm affect its performance.

Reputation and Efficiency: A Nonparametric Assessment of America's Top-Rated MBA Programs

Widely publicized reports of fresh MBAs getting multiple job offers with six-figure annual salaries leave a long-lasting general impression about the high quality of selected business schools. While such spectacular achievement in job placement rightly deserves recognition, one should not lose sight of the resources expended in order to accomplish this result. In this study, we employ a measure of Pareto-Koopmans global efficiency to evaluate the efficiency levels of the MBA programs in Business Week's top-rated list. We compute input- and output-oriented radial and non-radial efficiency measures for comparison. Among three tier groups, the schools from a higher tier group on average are more efficient than those from lower tiers, although variations in efficiency levels do occur within the same tier, which exist over different measures of efficiency.

Nonparametric Measures of Capacity Utilization: A Dual Approach

This paper develops a nonparametric method of obtaining the minimum of the long run average cost curve of a firm to define its capacity output. This provides a benchmark for measuring of capacity utilization at the observed output level of the firm. In the case of long run constant returns to scale, the minimum of the short run average cost curve is determined to measure short run capacity utilization. An empirical application measures yearly rates of capacity utilization in U.S. manufacturing over the period 1968-1998. Nonparametric determination of the short run average cost curve under variable returns to scale using an iterative search procedure is described in an appendix to this paper.

Secondary data analysis of lead screening compliance in high-risk children

Introduction. Despite the ban of lead-containing gasoline and paint, childhood lead poisoning remains a public health issue. Furthermore, a Medicaid-eligible child is 8 times more likely to have an elevated blood lead level (EBLL) than a non-Medicaid child, which is the primary reason for the early detection lead screening mandate for ages 12 and 24 months among the Medicaid population. Based on field observations, there was evidence that suggested a screening compliance issue. Objective. The purpose of this study was to analyze blood lead screening compliance in previously lead poisoned Medicaid children and test for an association between timely lead screening and timely childhood immunizations. The mean months between follow-up tests were also examined for a significant difference between the non-compliant and compliant lead screened children. Methods. Access to the surveillance data of all childhood lead poisoned cases in Bexar County was granted by the San Antonio Metropolitan Health District. A database was constructed and analyzed using descriptive statistics, logistic regression methods and non-parametric tests. Lead screening at 12 months of age was analyzed separately from lead screening at 24 months. The small portion of the population who were also related were included in one analysis and removed from a second analysis to check for significance. Gender, ethnicity, age of home, and having a sibling with an EBLL were ruled out as confounders for the association tests but ethnicity and age of home were adjusted in the nonparametric tests. Results. There was a strong significant association between lead screening compliance at 12 months and childhood immunization compliance, with or without including related children (p<0.00). However, there was no significant association between the two variables at the age of 24 months. Furthermore, there was no significant difference between the median of the mean months of follow-up blood tests among the non-compliant and compliant lead screened population for at the 12 month screening group but there was a significant difference at the 24 month screening group (p<0.01). Discussion. Descriptive statistics showed that 61% and 56% of the previously lead poisoned Medicaid population did not receive their 12 and 24 month mandated lead screening on time, respectively. This suggests that their elevated blood lead level may have been diagnosed earlier in their childhood. Furthermore, a child who is compliant with their lead screening at 12 months of age is 2.36 times more likely to also receive their childhood immunizations on time compared to a child who was not compliant with their 12 month screening. Even though there was no statistical significant association found for the 24 month group, the public health significance of a screening compliance issue is no less important. The Texas Medicaid program needs to enforce lead screening compliance because it is evident that there has been no monitoring system in place. Further recommendations include a need for an increased focus on parental education and the importance of taking their children for wellness exams on time.^

Assessment of the effect on statistical power of regression model misspecification by using techniques of mathematical statistics and simulation study

Objectives. This paper seeks to assess the effect on statistical power of regression model misspecification in a variety of situations. ^ Methods and results. The effect of misspecification in regression can be approximated by evaluating the correlation between the correct specification and the misspecification of the outcome variable (Harris 2010).In this paper, three misspecified models (linear, categorical and fractional polynomial) were considered. In the first section, the mathematical method of calculating the correlation between correct and misspecified models with simple mathematical forms was derived and demonstrated. In the second section, data from the National Health and Nutrition Examination Survey (NHANES 2007-2008) were used to examine such correlations. Our study shows that comparing to linear or categorical models, the fractional polynomial models, with the higher correlations, provided a better approximation of the true relationship, which was illustrated by LOESS regression. In the third section, we present the results of simulation studies that demonstrate overall misspecification in regression can produce marked decreases in power with small sample sizes. However, the categorical model had greatest power, ranging from 0.877 to 0.936 depending on sample size and outcome variable used. The power of fractional polynomial model was close to that of linear model, which ranged from 0.69 to 0.83, and appeared to be affected by the increased degrees of freedom of this model.^ Conclusion. Correlations between alternative model specifications can be used to provide a good approximation of the effect on statistical power of misspecification when the sample size is large. When model specifications have known simple mathematical forms, such correlations can be calculated mathematically. Actual public health data from NHANES 2007-2008 were used as examples to demonstrate the situations with unknown or complex correct model specification. Simulation of power for misspecified models confirmed the results based on correlation methods but also illustrated the effect of model degrees of freedom on power.^