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  <title>EconStor Collection:</title>
  <link rel="alternate" href="https://hdl.handle.net/10419/207758" />
  <subtitle />
  <id>https://hdl.handle.net/10419/207758</id>
  <updated>2026-04-28T11:25:34Z</updated>
  <dc:date>2026-04-28T11:25:34Z</dc:date>
  <entry>
    <title>On the nonparametric estimation of the conditional hazard estimator in a single functional index</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/266309" />
    <author>
      <name>Gagui, Abdelmalek</name>
    </author>
    <author>
      <name>Chouaf, Abdelhak</name>
    </author>
    <id>https://hdl.handle.net/10419/266309</id>
    <updated>2023-11-12T02:29:30Z</updated>
    <published>2022-01-01T00:00:00Z</published>
    <summary type="text">Title: On the nonparametric estimation of the conditional hazard estimator in a single functional index
Authors: Gagui, Abdelmalek; Chouaf, Abdelhak
Abstract: This paper deals with the conditional hazard estimator of a real response where the variable is given a functional random variable (i.e it takes values in an infinite-dimensional space). Specifically, we focus on the functional index model. This approach offers a good com- promise between nonparametric and parametric models. The principle aim is to prove the asymptotic normality of the proposed estimator under general conditions and in cases where the variables satisfy the strong mixing dependency. This was achieved by means of the kernel estimator method, based on a single-index structure. Finally, a simulation of our methodol- ogy shows that it is efficient for large sample sizes.</summary>
    <dc:date>2022-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Modified exponential time series model with prediction of total COVID-19 cases in Belgium, Czech Republic, Poland and Switzerland</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/266326" />
    <author>
      <name>Wachirapond Permpoonsinsup</name>
    </author>
    <author>
      <name>Rapin Sunthornwat</name>
    </author>
    <id>https://hdl.handle.net/10419/266326</id>
    <updated>2023-11-13T02:50:48Z</updated>
    <published>2022-01-01T00:00:00Z</published>
    <summary type="text">Title: Modified exponential time series model with prediction of total COVID-19 cases in Belgium, Czech Republic, Poland and Switzerland
Authors: Wachirapond Permpoonsinsup; Rapin Sunthornwat
Abstract: The coronavirus (COVID-19) pandemic affected every country worldwide. In particular, outbreaks in Belgium, the Czech Republic, Poland and Switzerland entered the second wave and was exponentially increasing between July and November, 2020. The aims of the study are: to estimate the compound growth rate, to develop a modified exponential time-series model compared with the hyperbolic time-series model, and to estimate the optimal parameters for the models based on the exponential least-squares, three selected points, partial-sums methods, and the hyperbolic least-squares for the daily COVID-19 cases in Belgium, the Czech Republic, Poland and Switzerland. The speed and spreading power of COVID-19 infections were obtained by using derivative and root-mean-squared methods, respectively. The results show that the exponential least-squares method was the most suitable for the parameter estimation. The compound growth rate of COVID-19 infection was the highest in Switzerland, and the speed and spreading power of COVID-19 infection were the highest in Poland between July and November, 2020.</summary>
    <dc:date>2022-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Long-term sovereign interest rates in Czechia, Hungary and Poland: A comparative assessment with an affine term structure model</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/266300" />
    <author>
      <name>Janus, Jakub</name>
    </author>
    <id>https://hdl.handle.net/10419/266300</id>
    <updated>2023-11-15T02:25:43Z</updated>
    <published>2022-01-01T00:00:00Z</published>
    <summary type="text">Title: Long-term sovereign interest rates in Czechia, Hungary and Poland: A comparative assessment with an affine term structure model
Authors: Janus, Jakub
Abstract: This paper provides a comparative evaluation of the behaviour of long-term sovereign yieldsin Czechia, Hungary and Poland from 2001 to 2019. An affine term structure model de-veloped by Adrian, Crump and Moench (2013) is used as an empirical framework for thedecomposition of the bond yields into term premium and risk-neutral components. We docu-ment a substantial compression in term premia which started in Central European economiesaround 2013 and played a decisive role in the changes that occurred in 10-year sovereignyields. This pattern, however, was more prevalent in Czechia and Poland than in Hun-gary. We show that long-term rates in all three economies remained higher than in Ger-many due to relatively large risk-neutral components. Nevertheless, cross-country corre-lations became increasingly dependent on term premium dynamics, both among CentralEuropean economies and between each of them and Germany. These results are robust tobias-correction in the baseline models and interpreted in the light of the general interest ratesdecline in the global economy. Potential policy implications are also discussed.</summary>
    <dc:date>2022-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Estimation of P (X È Y) for discrete distributions with non-identical support</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/266320" />
    <author>
      <name>Choudhury, Mriganka Mouli</name>
    </author>
    <author>
      <name>Bhattacharya, Rahul</name>
    </author>
    <author>
      <name>Maiti, Sudhansu S.</name>
    </author>
    <id>https://hdl.handle.net/10419/266320</id>
    <updated>2023-11-13T02:57:56Z</updated>
    <published>2022-01-01T00:00:00Z</published>
    <summary type="text">Title: Estimation of P (X È Y) for discrete distributions with non-identical support
Authors: Choudhury, Mriganka Mouli; Bhattacharya, Rahul; Maiti, Sudhansu S.
Abstract: The Uniformly Minimum Variance Unbiased (UMVU) and the Maximum Likelihood (ML) estimations of R = P(X È Y) and the associated variance are considered for independent discrete random variables X and Y. Assuming a discrete uniform distribution for X and the distribution of Y as a member of the discrete one parameter exponential family of distributions, theoretical expressions of such quantities are derived. Similar expressions are obtained when X and Y interchange their roles and both variables are from the discrete uniform distribution. A simulation study is carried out to compare the estimators numerically. A real application based on demand-supply system data is provided.</summary>
    <dc:date>2022-01-01T00:00:00Z</dc:date>
  </entry>
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