Burial Duration and Airpocket Explain Avalanche Survival Patterns in Austria

Abstract Business takeovers and new venture start-ups are two important and distinct entry modes of entrepreneurship. They differ from resource-based and organizational ecology perspectives. We compare firm survival patterns and determinants associated with the two entry modes. From two large French datasets, we find that business takeovers have a higher survival rate than new venture start-ups. However, these differences in survival probability reduce over the entrepreneurship life cycle and when controlling for different entrepreneur and firm characteristics. Moreover, we identify differences in determinants of survival for the two groups, highlighting a distinction between the two entrepreneurship entry modes. This work contributes to the literature on the relationship between entrepreneurship entry and firm survival, thereby contributing to both entrepreneurship and firm survival research.

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Survival patterns of Streptococcus suis serotypes 1 and 14 in porcine blood indicate cross-reactive bactericidal antibodies in naturally infected pigs

Veterinary Microbiology ◽

10.1016/j.vetmic.2021.109183 ◽

2021 ◽

pp. 109183

Author(s):

L. Mayer ◽

N. Bornemann ◽

S. Lehnert ◽

A. de Greeff ◽

K. Strutzberg-Minder ◽

...

Keyword(s):

Streptococcus Suis ◽

Porcine Blood ◽

Survival Patterns

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Characteristics and Survival Patterns of Solid Organ Transplant Patients Developing De Novo Colon and Rectal Cancer

Diseases of the Colon & Rectum ◽

10.1007/s10350-004-0674-0 ◽

2004 ◽

Vol 47 (11) ◽

pp. 1898-1903 ◽

Cited By ~ 42

Author(s):

Harry T. Papaconstantinou ◽

Bradford Sklow ◽

Michael J. Hanaway ◽

Thomas G. Gross ◽

Thomas M. Beebe ◽

...

Keyword(s):

Rectal Cancer ◽

De Novo ◽

Organ Transplant ◽

Solid Organ Transplant ◽

Solid Organ ◽

Transplant Patients ◽

Colon And Rectal Cancer ◽

Survival Patterns

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Survival Analysis by Penalized Regression and Matrix Factorization

The Scientific World JOURNAL ◽

10.1155/2013/632030 ◽

2013 ◽

Vol 2013 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Yeuntyng Lai ◽

Morihiro Hayashida ◽

Tatsuya Akutsu

Keyword(s):

Matrix Factorization ◽

Regression Models ◽

Nonnegative Matrix ◽

B Cell Lymphoma ◽

Penalized Regression ◽

Survival Prediction ◽

Suitable Model ◽

Gene Expressions ◽

Combination Methods ◽

Survival Patterns

Because every disease has its unique survival pattern, it is necessary to find a suitable model to simulate followups. DNA microarray is a useful technique to detect thousands of gene expressions at one time and is usually employed to classify different types of cancer. We propose combination methods of penalized regression models and nonnegative matrix factorization (NMF) for predicting survival. We triedL1- (lasso),L2- (ridge), andL1-L2combined (elastic net) penalized regression for diffuse large B-cell lymphoma (DLBCL) patients' microarray data and found thatL1-L2combined method predicts survival best with the smallest logrankPvalue. Furthermore, 80% of selected genes have been reported to correlate with carcinogenesis or lymphoma. Through NMF we found that DLBCL patients can be divided into 4 groups clearly, and it implies that DLBCL may have 4 subtypes which have a little different survival patterns. Next we excluded some patients who were indicated hard to classify in NMF and executed three penalized regression models again. We found that the performance of survival prediction has been improved with lower logrankPvalues. Therefore, we conclude that after preselection of patients by NMF, penalized regression models can predict DLBCL patients' survival successfully.

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