A comparison of in-sample forecasting methods

In-sample forecasting is a recent continuous modification of well-known forecasting methods based on aggregated data. These aggregated methods are known as age-cohort methods in demography, economics, epidemiology and sociology and as chain ladder in non-life insurance. Data is organized in a two-way table with age and cohort as indices, but without measures of exposure. It has recently been established that such structured forecasting methods based on aggregated data can be interpreted as structured histogram estimators. Continuous in-sample forecasting transfers these classical forecasting models into a modern statistical world including smoothing methodology that is more efficient than smoothing via histograms. All in-sample forecasting estimators are collected and their performance is compared via a finite sample simulation study. All methods are extended via multiplicative bias correction. Asymptotic theory is being developed for the histogram-type method of sieves and for the multiplicatively corrected estimators. The multiplicative bias corrected estimators improve all other known in-sample forecasters in the simulation study. The density projection approach seems to have the best performance with forecasting based on survival densities being the runner-up

Pre-transplant reduction of isohaemagglutinin titres by donor group plasma infusion does not reduce the incidence of pure red cell aplasia in major ABO-mismatched transplants

Major ABO incompatibility in stem cell transplant recipients has been associated with pure red cell aplasia (PRCA). Reduction of incompatible isohaemagglutinin titres pre-transplant by various methods has been thought to reduce the incidence of PRCA. Our data suggest that pre-transplant reduction of incompatible isohaemagglutinin titres by donor group plasma infusion does not reduce the incidence of PRCA. We also failed to find any relationship between pre-transplant ABO isohaemagglutinin titre and the risk of developing PRCA

PAC-Bayesian Bounds for Randomized Empirical Risk Minimizers

The aim of this paper is to generalize the PAC-Bayesian theorems proved by Catoni in the classification setting to more general problems of statistical inference. We show how to control the deviations of the risk of randomized estimators. A particular attention is paid to randomized estimators drawn in a small neighborhood of classical estimators, whose study leads to control the risk of the latter. These results allow to bound the risk of very general estimation procedures, as well as to perform model selection

Development of a Fuel Quantity based Engine Control Unit Software Architecture

   Conventionally diesel engines are controlled in open loop with maps based on engine speed and throttle position wherein fuel quantity is indirectly fixed using the rail pressure and injection duration maps with engine speed and throttle position as the independent variables which are measured by the respective sensors. In this work an engine control unit (ECU) software architecture where fuel quantity is directly specified in relation to the driver demand was implemented by modifying the control logic of a throttle position based framework. A desired fuel quantity for a given engine speed and throttle position was mapped from base line experiments on the reference engine. Injection durations and rail pressure required for this quantity was mapped on a fuel injector calibration test bench. The final calculation of injection duration in the new architecture is calculated using the fuel injector model. This enables determination of fuel quantity injected at any moment which directly indicates the torque produced by the engine at a given speed enabling smoke limited fuelling calculations and easing the implementation of control functions like all-speed governing

An Investigation of Work-Related Stress among High School Teachers in the Hhohho Region of Swaziland

This study sought to investigate the work-related stress among high school teachers in the Hhohho region of Swaziland. It followed the descriptive-correlation research design and adopted the Person-Environment Fit theory. The target population of this study was all qualified teachers teaching in high schools in the Hhohho region of Swaziland. The sampling procedure that was employed to select the schools and the teachers to participate in this investigation was simple random sampling. A pilot testing was conducted. Validity and reliability of instruments were attended to. Required permissions were obtained from relevant authorities and the sample gave informed consent. Questionnaire response rate was 82%. Quantitative data were analysed using the Statistical Package for the Social Sciences (SPSS) version 10.0. The findings of the study showed that high school teachers in the Hhohho region of Swaziland are moderately stressed. Relevant recommendations are given. DOI: 10.5901/mjss.2014.v5n15p57

Longitudinal Patterns of Mexican and Puerto Rican Children’s Asthma Controller Medication Adherence and Acute Healthcare Utilization

Rationale: Researchers tend to study Latinos as a single group but recent asthma research confirmed differences among Latino subgroups. Variations in controller medication adherence may be a factor in the observed health disparities between Mexican and Puerto Rican children. Adherence is not a stable phenomenon, however, there is a paucity of data on patterns of adherence, sociodemographic predictors of patterns, and variations in asthma-related acute healthcare utilization by adherence pattern among Latino sub-groups. Objectives: Identify patterns of inhaled corticosteroid medication adherence over twelve months among Mexican and Puerto Rican children with persistent asthma; examine sociodemographic predictors of adherence patterns by ethnicity; and investigate asthma-related acute healthcare utilization based on these patterns. Methods: We analyzed controller medication Doser data from Mexican and Puerto Rican children (n=123; ages 5-12 years) with persistent asthma who participated with their caregivers in a longitudinal, non-intervention study (Phoenix, AZ and Bronx, NY). Interview and medical record data were collected at enrollment, 3, 6, 9, and 12 months post-enrollment. Results: 47%-53% of children had poor adherence ( Conclusions: This study demonstrated that unique ethnicity within Latino populations may be associated with different risk levels for suboptimal controller medication adherence which may be a factor in the observed asthma health disparities between Mexican and Puerto Rican children. Increased understanding of and attention to children’s controller medication adherence patterns will provide evidence needed to identify children at highest risk for acute healthcare utilization and offer more intensive intervention using less-intensive approaches for those at low risk

A General Framework for Constrained Smoothing

There are a wide array of smoothing methods available for finding structure in data. A general framework is developed which shows that many of these can be viewed as a projection of the data, with respect to appropriate norms. The underlying vector space is an unusually large product space, which allows inclusion of a wide range of smoothers in our setup (including many methods not typically considered to be projections). We give several applications of this simple geometric interpretation of smoothing. A major payoff is the natural and computationally frugal incorporation of constraints. Our point of view also motivates new estimates and it helps to understand the finite sample and asymptotic behaviour of these estimates

A Path Algorithm for Constrained Estimation

Many least squares problems involve affine equality and inequality constraints. Although there are variety of methods for solving such problems, most statisticians find constrained estimation challenging. The current paper proposes a new path following algorithm for quadratic programming based on exact penalization. Similar penalties arise in $l_1$ regularization in model selection. Classical penalty methods solve a sequence of unconstrained problems that put greater and greater stress on meeting the constraints. In the limit as the penalty constant tends to $\infty$, one recovers the constrained solution. In the exact penalty method, squared penalties are replaced by absolute value penalties, and the solution is recovered for a finite value of the penalty constant. The exact path following method starts at the unconstrained solution and follows the solution path as the penalty constant increases. In the process, the solution path hits, slides along, and exits from the various constraints. Path following in lasso penalized regression, in contrast, starts with a large value of the penalty constant and works its way downward. In both settings, inspection of the entire solution path is revealing. Just as with the lasso and generalized lasso, it is possible to plot the effective degrees of freedom along the solution path. For a strictly convex quadratic program, the exact penalty algorithm can be framed entirely in terms of the sweep operator of regression analysis. A few well chosen examples illustrate the mechanics and potential of path following.Comment: 26 pages, 5 figure

Differencing techniques in semi-parametric panel data varying coefficient models with fixed effects: a Monte Carlo study.

Recently, some new techniques have been proposed for the estimation of semi-parametric fixed effects varying coefficient panel data models. These new techniques fall within the class of the so-called differencing estimators. In particular, we consider first-differences and within local linear regression estimators. Analyzing their asymptotic properties it turns out that, keeping the same order of magnitude for the bias term, these estimators exhibit different asymptotic bounds for the variance. In both cases, the consequences are suboptimal non-parametric rates of convergence. In order to solve this problem, by exploiting the additive structure of this model, a one-step backfitting algorithm is proposed. Under fairly general conditions, it turns out that the resulting estimators show optimal rates of convergence and exhibit the oracle efficiency property. Since both estimators are asymptotically equivalent, it is of interest to analyze their behavior in small sample sizes. In a fully parametric context, it is well-known that, under strict exogeneity assumptions the performance of both first-differences and within estimators is going to depend on the stochastic structure of the idiosyncratic random errors. However, in the non-parametric setting, apart from the previous issues other factors such as dimensionality or sample size are of great interest. In particular, we would be interested in learning about their relative average mean square error under different scenarios. The simulation results basically confirm the theoretical findings for both local linear regression and one-step backfitting estimators. However, we have found out that within estimators are rather sensitive to the size of number of time observations