1,899 research outputs found
Ask questions, get sales : close the deak and create long-term relationships / Stephan Schiffman.
No full textIncludes index.v, 168 pages ;In Ask Questions, Get Sales, the author and sales guru Stephan Schiffman helps readers boost their careers to the gold-medal level by teaching them how to strengthen their questioning skills during the sales process. The premise is simple yet effective: In order to be successful, salespeople need to change their mindset from "need-orientated" to "do-orientated". The message of the book centers around six core "do" questions: What do you do? How do you do it? When and where do you do it? Why do you do it that way? Who do you do it with? How can we help you do it better? With this indispensable guide in their briefcase, salespeople will have information at the ready to score big sales over the short term and the long term
Unemployment Benefits and Unemployment Rates of Low-Skilled and Elder Workers in West Germany: A Search Equilibrium Approach
No full textApproach Author & abstract Download 16 References 1 Citations Related works & more Corrections Author Listed: Launov, Andrey ([email protected]) (University of Kent) Wolff, Joachim ([email protected]) (Institute for Employment Research (IAB), Nuremberg) Klasen, Stephan ([email protected]) (University of Göttingen) Registered: Stephan Klasen Abstract In this paper we investigate whether the extension of the entitlement to unemployment benefits in the mid 80s can explain the increase in the unemployment rates of unskilled and elder workers in western Germany. To answer this question we estimate a version of the Burdett-Mortensen search equilibrium model and analyze how workers’ search behaviour responded to these reforms. We try both nonparametric and fully-parametric estimation methods and identify the cases in which the nonparametric approach cannot be applied. We find that the entitlement reforms are largely responsible for the increase of unemployment among unskilled workers
Unemployment Benefits and Unemployment Rates of Low-Skilled and Elder Workers in West Germany: A Search Equilibrium Approach
No full textApproach Author & abstract Download 16 References 1 Citations Related works & more Corrections Author Listed: Launov, Andrey ([email protected]) (University of Kent) Wolff, Joachim ([email protected]) (Institute for Employment Research (IAB), Nuremberg) Klasen, Stephan ([email protected]) (University of Göttingen) Registered: Stephan Klasen Abstract In this paper we investigate whether the extension of the entitlement to unemployment benefits in the mid 80s can explain the increase in the unemployment rates of unskilled and elder workers in western Germany. To answer this question we estimate a version of the Burdett-Mortensen search equilibrium model and analyze how workers’ search behaviour responded to these reforms. We try both nonparametric and fully-parametric estimation methods and identify the cases in which the nonparametric approach cannot be applied. We find that the entitlement reforms are largely responsible for the increase of unemployment among unskilled workers
RPPanalyzer: Analysis of reverse-phase protein array data
No full textAbstract
Summary: RPPanalyzer is a statistical tool developed to read reverse-phase protein array data, to perform the basic data analysis and to visualize the resulting biological information. The R-package provides different functions to compare protein expression levels of different samples and to normalize the data. Implemented plotting functions permit a quality control by monitoring data distribution and signal validity. Finally, the data can be visualized in heatmaps, boxplots, time course plots and correlation plots. RPPanalyzer is a flexible tool and tolerates a huge variety of different experimental designs.
Availability: The RPPAanalyzer is open source and freely available as an R-Package on the CRAN platform http://cran.r-project.org/
Contact: [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p
Measuring Vulnerability to Poverty Using Long-Term Panel Data
No full textMeasuring Vulnerability to Poverty Using Long-Term Panel Data Author & abstract Download & other version 16 References 4 Citations Related works & more Corrections Author Listed: Katja Landau (Georg-August-University Göttingen) Stephan Klasen (Georg-August-University Göttingen) Walter Zucchini (Georg-August-University Göttingen) Registered: Stephan Klasen Abstract We investigate the accuracy of ex ante assessments of vulnerability to income poverty using cross-sectional data and panel data. We use long-term panel data from Germany and apply di fferent regression models, based on household covariates and previous-year equivalence income, to classify a household as vulnerable or not. Predictive performance is assessed using the Receiver Operating Characteristics (ROC), which takes account of false positive as well as true positive rates. Estimates based on cross-sectional data are much less accurate than those based on panel data, but for Germany, the accuracy of vulnerability predictions is limited even when panel data are used. In part this low accuracy is due to low poverty incidence and high mobility in and out of poverty
Measuring Vulnerability to Poverty Using Long-Term Panel Data
No full textMeasuring Vulnerability to Poverty Using Long-Term Panel Data Author & abstract Download & other version 16 References 4 Citations Related works & more Corrections Author Listed: Katja Landau (Georg-August-University Göttingen) Stephan Klasen (Georg-August-University Göttingen) Walter Zucchini (Georg-August-University Göttingen) Registered: Stephan Klasen Abstract We investigate the accuracy of ex ante assessments of vulnerability to income poverty using cross-sectional data and panel data. We use long-term panel data from Germany and apply di fferent regression models, based on household covariates and previous-year equivalence income, to classify a household as vulnerable or not. Predictive performance is assessed using the Receiver Operating Characteristics (ROC), which takes account of false positive as well as true positive rates. Estimates based on cross-sectional data are much less accurate than those based on panel data, but for Germany, the accuracy of vulnerability predictions is limited even when panel data are used. In part this low accuracy is due to low poverty incidence and high mobility in and out of poverty
Graph based fusion of high-dimensional gene- and microRNA expression data
No full textOne of the main goals in cancer studies including high-throughput microRNA
(miRNA) and mRNA data is to find and assess prognostic signatures capable
of predicting clinical outcome. Both mRNA and miRNA expression changes in
cancer diseases are described to reflect clinical characteristics like staging and
prognosis. Furthermore, miRNA abundance can directly affect target transcripts
and translation in tumor cells. Prediction models are trained to identify either
mRNA or miRNA signatures for patient stratification. With the increasing
number of microarray studies collecting mRNA and miRNA from the same
patient cohort there is a need for statistical methods to integrate or fuse both
kinds of data into one prediction model in order to find a combined signature
that improves the prediction.
Here, we propose a new method to fuse miRNA and mRNA data into one
prediction model. Since miRNAs are known regulators of mRNAs, correlations
between miRNA and mRNA expression data as well as target prediction
information were used to build a bipartite graph representing the relations
between miRNAs and mRNAs.
Feature selection is a critical part when fitting prediction models to high-
dimensional data. Most methods treat features, in this case genes or miRNAs,
as independent, an assumption that does not hold true when dealing with
combined gene and miRNA expression data. To improve prediction accuracy, a
description of the correlation structure in the data is needed. In this work the
bipartite graph was used to guide the feature selection and therewith improve
prediction results and find a stable prognostic signature of miRNAs and genes.
The method is evaluated on a prostate cancer data set comprising 98 patient
samples with miRNA and mRNA expression data. The biochemical relapse, an
important event in prostate cancer treatment, was used as clinical endpoint.
Biochemical relapse coins the renewed rise of the blood level of a prostate
marker (PSA) after surgical removal of the prostate. The relapse is a hint
for metastases and usually the point in clinical practise to decide for further
treatment.
A boosting approach was used to predict the biochemical relapse. It could
be shown that the bipartite graph in combination with miRNA and mRNA
expression data could improve prediction performance. Furthermore the ap-
proach improved the stability of the feature selection and therewith yielded
more consistent marker sets. Of course, the marker sets produced by this new
method contain mRNAs as well as miRNAs.
The new approach was compared to two state-of-the-art methods suited for
high-dimensional data and showed better prediction performance in both cases
Evaluation of in-store processes related to returnable packaging services offered in grocery stores - the store management perspective
No full textAuthor Stephan LehnerMasterarbeit Universität Linz 202
Evaluation of in-store processes related to returnable packaging services offered in grocery stores - the store management perspective
No full textAuthor Stephan LehnerMasterarbeit Universität Linz 202
Integration of pathway knowledge into a reweighted recursive feature elimination approach for risk stratification of cancer patients
No full textAbstract
Motivation: One of the main goals of high-throughput gene-expression studies in cancer research is to identify prognostic gene signatures, which have the potential to predict the clinical outcome. It is common practice to investigate these questions using classification methods. However, standard methods merely rely on gene-expression data and assume the genes to be independent. Including pathway knowledge a priori into the classification process has recently been indicated as a promising way to increase classification accuracy as well as the interpretability and reproducibility of prognostic gene signatures.
Results: We propose a new method called Reweighted Recursive Feature Elimination. It is based on the hypothesis that a gene with a low fold-change should have an increased influence on the classifier if it is connected to differentially expressed genes. We used a modified version of Google's PageRank algorithm to alter the ranking criterion of the SVM-RFE algorithm. Evaluations of our method on an integrated breast cancer dataset comprising 788 samples showed an improvement of the area under the receiver operator characteristic curve as well as in the reproducibility and interpretability of selected genes.
Availability: The R code of the proposed algorithm is given in Supplementary Material.
Contact: [email protected]; [email protected]
Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p
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