Finite sample nonparametric tests for linear regressions

We introduce several exact nonparametric tests for finite sample multivariatelinear regressions, and compare their powers. This fills an important gap inthe literature where the only known nonparametric tests are either asymptotic,or assume one covariate only.

Solving non-linear stochastic models by parameterizing expectations: An application to asset pricing with production

A new algorithm called the parameterized expectations approach(PEA) for solving dynamic stochastic models under rational expectationsis developed and its advantages and disadvantages are discussed. Thisalgorithm can, in principle, approximate the true equilibrium arbitrarilywell. Also, this algorithm works from the Euler equations, so that theequilibrium does not have to be cast in the form of a planner's problem.Monte--Carlo integration and the absence of grids on the state variables,cause the computation costs not to go up exponentially when the numberof state variables or the exogenous shocks in the economy increase. \\As an application we analyze an asset pricing model with endogenousproduction. We analyze its implications for time dependence of volatilityof stock returns and the term structure of interest rates. We argue thatthis model can generate hump--shaped term structures.

Evaluating predictive densities of U.S. output growth and inflation in a large macroeconomic data set

We evaluate conditional predictive densities for U.S. output growth and inflationusing a number of commonly used forecasting models that rely on a large number ofmacroeconomic predictors. More specifically, we evaluate how well conditional predictive densities based on the commonly used normality assumption fit actual realizationsout-of-sample. Our focus on predictive densities acknowledges the possibility that, although some predictors can improve or deteriorate point forecasts, they might have theopposite effect on higher moments. We find that normality is rejected for most modelsin some dimension according to at least one of the tests we use. Interestingly, however,combinations of predictive densities appear to be correctly approximated by a normaldensity: the simple, equal average when predicting output growth and Bayesian modelaverage when predicting inflation.

Fate of linear and supercoiled multinucleosomic templates during transcription.

Electron microscopy was used to monitor the fate of reconstituted nucleosome cores during in vitro transcription of long linear and supercoiled multinucleosomic templates by the prokaryotic T7 RNA polymerase and the eukaryotic RNA polymerase II. Transcription by T7 RNA polymerase disrupted the nucleosomal configuration in the transcribed region, while nucleosomes were preserved upstream of the transcription initiation site and in front of the polymerase. Nucleosome disruption was independent of the topology of the template, linear or supercoiled, and of the presence or absence of nucleosome positioning sequences in the transcribed region. In contrast, the nucleosomal configuration was preserved during transcription from the vitellogenin B1 promoter with RNA polymerase II in a rat liver total nuclear extract. However, the persistence of nucleosomes on the template was not RNA polymerase II-specific, but was dependent on another activity present in the nuclear extract. This was demonstrated by addition of the extract to the T7 RNA polymerase transcription reaction, which resulted in retention of the nucleosomal configuration. This nuclear activity, also found in HeLa cell nuclei, is heat sensitive and could not be substituted by nucleoplasmin, chromatin assembly factor (CAF-I) or a combination thereof. Altogether, these results identify a novel nuclear activity, called herein transcription-dependent chromatin stabilizing activity I or TCSA-I, which may be involved in a nucleosome transfer mechanism during transcription.

LIF and sIL-2R plasma concentrations in IVF patients on the day of embryo transfer: predictive markers of IVF outcome.

Successful implantation is still the limiting step in IVF. We hypothesized that maternal plasma concentrations of certain cytokines at the time of embryo transfer could predict the likelihood of successful implantation and pregnancy. sIL-2R, IL-6, LIF, and MMP2 concentrations were measured in plasma from 160 IVF patients (natural and stimulated IVF cycles) on the morning of the embryo transfer (ET0) and 14days later (ET+14). Patients were ultimately subdivided into four groups depending on the IVF treatment outcome (pregnancy failure, biochemical pregnancy, first-trimester miscarriage and normal term delivery). In natural and stimulated IVF cycles at ET0, sIL-2R concentrations were threefold higher in biochemical pregnancies than in pregnancy failures (P=0.020), and in natural cycles only, 2.5-fold higher in normal term deliveries than in pregnancy failures (P=0.023). Conversely, in natural and stimulated IVF cycles at ET0, LIF concentrations were one third lower in biochemical pregnancies/first-trimester miscarriages compared with pregnancy failures (P=0.042). We suggest that high sIL-2R and low LIF concentrations in maternal plasma on the morning of the embryo transfer might be associated with increased risks of early pregnancy loss, while a basal level of sIL-2R is necessary for normal term delivery outcome. Both cytokine measurements might therefore be useful in the management of IVF patients, and modulation of their concentrations could be investigated as a therapeutic alternative for women with abnormal concentrations at the time of embryo transfer.

Testing for convergence clubs in income per-capita: A predictive density approach

The paper proposes a technique to jointly test for groupings of unknown size in the cross sectional dimension of a panel and estimates the parameters of each group, and applies it to identifying convergence clubs in income per-capita. The approach uses the predictive density of the data, conditional on the parameters of the model. The steady state distribution of European regional data clusters around four poles of attraction with different economic features. The distribution of incomeper-capita of OECD countries has two poles of attraction and each grouphas clearly identifiable economic characteristics.

MGMT promoter methylation is prognostic but not predictive for outcome to adjuvant PCV chemotherapy in anaplastic oligodendroglial tumors: a report from EORTC Brain Tumor Group Study 26951.

PURPOSE: O6-methylguanine-methyltransferase (MGMT) promoter methylation has been shown to predict survival of patients with glioblastomas if temozolomide is added to radiotherapy (RT). It is unknown if MGMT promoter methylation is also predictive to outcome to RT followed by adjuvant procarbazine, lomustine, and vincristine (PCV) chemotherapy in patients with anaplastic oligodendroglial tumors (AOT). PATIENTS AND METHODS: In the European Organisation for the Research and Treatment of Cancer study 26951, 368 patients with AOT were randomly assigned to either RT alone or to RT followed by adjuvant PCV. From 165 patients of this study, formalin-fixed, paraffin-embedded tumor tissue was available for MGMT promoter methylation analysis. This was investigated with methylation specific multiplex ligation-dependent probe amplification. RESULTS: In 152 cases, an MGMT result was obtained, in 121 (80%) cases MGMT promoter methylation was observed. Methylation strongly correlated with combined loss of chromosome 1p and 19q loss (P = .00043). In multivariate analysis, MGMT promoter methylation, 1p/19q codeletion, tumor necrosis, and extent of resection were independent prognostic factors. The prognostic significance of MGMT promoter methylation was equally strong in the RT arm and the RT/PCV arm for both progression-free survival and overall survival. In tumors diagnosed at central pathology review as glioblastoma, no prognostic effect of MGMT promoter methylation was observed. CONCLUSION: In this study, on patients with AOT MGMT promoter methylation was of prognostic significance and did not have predictive significance for outcome to adjuvant PCV chemotherapy. The biologic effect of MGMT promoter methylation or pathogenetic features associated with MGMT promoter methylation may be different for AOT compared with glioblastoma.

Otimização Não Linear de Mínimos Quadrados

O problema de otimização de mínimos quadrados e apresentado como uma classe importante de problemas de minimização sem restrições. A importância dessa classe de problemas deriva das bem conhecidas aplicações a estimação de parâmetros no contexto das analises de regressão e de resolução de sistemas de equações não lineares. Apresenta-se uma revisão dos métodos de otimização de mínimos quadrados lineares e de algumas técnicas conhecidas de linearização. Faz-se um estudo dos principais métodos de gradiente usados para problemas não lineares gerais: Métodos de Newton e suas modificações incluindo os métodos Quasi-Newton mais usados (DFP e BFGS). Introduzem-se depois métodos específicos de gradiente para problemas de mínimos quadrados: Gauss-Newton e Levenberg-Larquardt. Apresenta-se uma variedade de exemplos selecionados na literatura para testar os diferentes métodos usando rotinas MATLAB. Faz-se uma an alise comparativa dos algoritmos baseados nesses ensaios computacionais que exibem as vantagens e desvantagens dos diferentes métodos.

Combining multivariate density forecasts using predictive criteria

This paper combines multivariate density forecasts of output growth, inflationand interest rates from a suite of models. An out-of-sample weighting scheme based onthe predictive likelihood as proposed by Eklund and Karlsson (2005) and Andersson andKarlsson (2007) is used to combine the models. Three classes of models are considered: aBayesian vector autoregression (BVAR), a factor-augmented vector autoregression (FAVAR)and a medium-scale dynamic stochastic general equilibrium (DSGE) model. Using Australiandata, we find that, at short forecast horizons, the Bayesian VAR model is assignedthe most weight, while at intermediate and longer horizons the factor model is preferred.The DSGE model is assigned little weight at all horizons, a result that can be attributedto the DSGE model producing density forecasts that are very wide when compared withthe actual distribution of observations. While a density forecast evaluation exercise revealslittle formal evidence that the optimally combined densities are superior to those from thebest-performing individual model, or a simple equal-weighting scheme, this may be a resultof the short sample available.

On the throughput-WIP trade-off in queueing systems, diminishing returns and the threshold property: A linear programming approach

We present a new unifying framework for investigating throughput-WIP(Work-in-Process) optimal control problems in queueing systems,based on reformulating them as linear programming (LP) problems withspecial structure: We show that if a throughput-WIP performance pairin a stochastic system satisfies the Threshold Property we introducein this paper, then we can reformulate the problem of optimizing alinear objective of throughput-WIP performance as a (semi-infinite)LP problem over a polygon with special structure (a thresholdpolygon). The strong structural properties of such polygones explainthe optimality of threshold policies for optimizing linearperformance objectives: their vertices correspond to the performancepairs of threshold policies. We analyze in this framework theversatile input-output queueing intensity control model introduced byChen and Yao (1990), obtaining a variety of new results, including (a)an exact reformulation of the control problem as an LP problem over athreshold polygon; (b) an analytical characterization of the Min WIPfunction (giving the minimum WIP level required to attain a targetthroughput level); (c) an LP Value Decomposition Theorem that relatesthe objective value under an arbitrary policy with that of a giventhreshold policy (thus revealing the LP interpretation of Chen andYao's optimality conditions); (d) diminishing returns and invarianceproperties of throughput-WIP performance, which underlie thresholdoptimality; (e) a unified treatment of the time-discounted andtime-average cases.

Exact tests for correlation and for the slope in simple linear regressions without making assumptions

We present an exact test for whether two random variables that have known bounds on their support are negatively correlated. The alternative hypothesis is that they are not negatively correlated. No assumptions are made on the underlying distributions. We show by example that the Spearman rank correlation test as the competing exact test of correlation in nonparametric settings rests on an additional assumption on the data generating process without which it is not valid as a test for correlation.We then show how to test for the significance of the slope in a linear regression analysis that invovles a single independent variable and where outcomes of the dependent variable belong to a known bounded set.