|
|
The uniformly most powerful test. The uniformly most powerful unbiased test
Sečkárová, Vladimíra ; Juríček, Jozef (advisor) ; Jurečková, Jana (referee)
Nazov prace: Stejuomrrne ncjsilnejsi test. Stejnomerne nojsilnejsi nestranny test Autor: Vladimira Seckarova Katcdra: Katedra pravdepodobnosti a matematickej statistiky Veduci bakalarskoj prace: Mgr. .Jozef Juricek e-mail voduceho: jurijlam@artax.karlin.mff.cuni.cz Al>strakt: Tato prtica sa zaobera prol)letnatikou testovania hypotez. konkretne existenciou rovnonierne najsilnejsieho a rovnomerne najsilnej.sicho nestrauueho testu. Prva kapitola olisahuje zakladno pojiny testovania hypote/. V druhej ka- pitole jo zayodcny pojein rovnomerne najsilnejsieho testu ako i jeho odvodenie v roznyeh obeenych ]>ripadoch i pro pa.rametre normalneho roxdelenia. Trctia. ka- pitola sa zaobera rovnomerne najsilnejsini ncstrannyin testom a jeho odvodonim obocnc a aj ])re parn.met.ro normalneho ro/,delenia. KlYieove .slov;i: testovanie hypotez, najsilnejsi test, rovnomerne naj.silnejsi test, rovnomerne najsilnejsi nc.stranny test Title: Uniformly most powerful test. Uniformly most powerful unbiased test Author: Vladimira Seckarova Department: Department of Probability and Mathematical Statistics Supervisor: Mgr. .lozef .luricek Supervisor's e-mail address: jurijlam@artax.karlin.mff.cuni.cz Abstract: In this work we study problems of hypotheses testing, particularly exis- tence of the uniformly most powerful and the uniformly...
|
|
|
Classification analysis
Rensová, Dita ; Kalina, Jan (advisor) ; Juríček, Jozef (referee)
Xazev pracc: Kiasifikacni analy/a Autor: Dita. Rensova Kak'dra: Katedra. pravdepodobnost i a ma.tema.ticke statist iky Vedouci ba.kalarske prace: Ur.rer.nat. Ja.n Kaliua e-mail vedoucfho; kalina n-karlin.inff.cuni.c7 Al)stvakt: V toto pnici sc- zabvvame modely klasifikacm analyzy. Popiseme jednotliva klasiiikacni pravidla, a son\islosti mexi niini. Nejprve sc zaniefhne na modely line-arm a kvadraticke klasifikaee pru pripad dvou sknpin. kterr dak1 xobccninu: na, linearni a. kvadi'at.icke inodcly pro pn'pad kla,sifikae.e do vice skupin. Pole so bndeine /abyvat pra\xlepodobnosti s[>a.tnc klasifikaeo nrcit(''ho objektu do sknpiny a.mettxlami, jak Into pravdepodobnost odhad- nont. Dale se zmhn'nie o vyu/iti diskriniinacnieh skc'irii pri kiasifikaei a so- ziianiune se s modoloni logist.ieke kla.sifika.ee. Na zaver pfetlvedi^iuj ponzitf nekt.erych vy!)ranyeli modclii na, konkn'M nieli dateeh / oborn lesnictvi. Klieova slova: Lincarni klasifikace. kvadraticka klasilikacc, lo^ist.icka klasi- fikace. diskrhninacc a klasifikace Title: Cla.ssifica.t ion analysis Author; Uita Hensova Depaituiont,: Department ol Probability and Mathematical Statistics Supervisor: IJr.rer.nat. .Jan Kalina Supervisor's e-mail address: kalina:(i;karhn.mfl.cuni.c/ Abstract.: In the- present work we study methods for classification analysis....
|
|
| |
|
| |
|
|
Classification analysis
Rensová, Dita ; Juríček, Jozef (referee) ; Kalina, Jan (advisor)
Xazev pracc: Kiasifikacni analy/a Autor: Dita. Rensova Kak'dra: Katedra. pravdepodobnost i a ma.tema.ticke statist iky Vedouci ba.kalarske prace: Ur.rer.nat. Ja.n Kaliua e-mail vedoucfho; kalina n-karlin.inff.cuni.c7 Al)stvakt: V toto pnici sc- zabvvame modely klasifikacm analyzy. Popiseme jednotliva klasiiikacni pravidla, a son\islosti mexi niini. Nejprve sc zaniefhne na modely line-arm a kvadraticke klasifikaee pru pripad dvou sknpin. kterr dak1 xobccninu: na, linearni a. kvadi'at.icke inodcly pro pn'pad kla,sifikae.e do vice skupin. Pole so bndeine /abyvat pra\xlepodobnosti s[>a.tnc klasifikaeo nrcit(''ho objektu do sknpiny a.mettxlami, jak Into pravdepodobnost odhad- nont. Dale se zmhn'nie o vyu/iti diskriniinacnieh skc'irii pri kiasifikaei a so- ziianiune se s modoloni logist.ieke kla.sifika.ee. Na zaver pfetlvedi^iuj ponzitf nekt.erych vy!)ranyeli modclii na, konkn'M nieli dateeh / oborn lesnictvi. Klieova slova: Lincarni klasifikace. kvadraticka klasilikacc, lo^ist.icka klasi- fikace. diskrhninacc a klasifikace Title: Cla.ssifica.t ion analysis Author; Uita Hensova Depaituiont,: Department ol Probability and Mathematical Statistics Supervisor: IJr.rer.nat. .Jan Kalina Supervisor's e-mail address: kalina:(i;karhn.mfl.cuni.c/ Abstract.: In the- present work we study methods for classification analysis....
|
|
|
The uniformly most powerful test. The uniformly most powerful unbiased test
Sečkárová, Vladimíra ; Jurečková, Jana (referee) ; Juríček, Jozef (advisor)
Nazov prace: Stejuomrrne ncjsilnejsi test. Stejnomerne nojsilnejsi nestranny test Autor: Vladimira Seckarova Katcdra: Katedra pravdepodobnosti a matematickej statistiky Veduci bakalarskoj prace: Mgr. .Jozef Juricek e-mail voduceho: jurijlam@artax.karlin.mff.cuni.cz Al>strakt: Tato prtica sa zaobera prol)letnatikou testovania hypotez. konkretne existenciou rovnonierne najsilnejsieho a rovnomerne najsilnej.sicho nestrauueho testu. Prva kapitola olisahuje zakladno pojiny testovania hypote/. V druhej ka- pitole jo zayodcny pojein rovnomerne najsilnejsieho testu ako i jeho odvodenie v roznyeh obeenych ]>ripadoch i pro pa.rametre normalneho roxdelenia. Trctia. ka- pitola sa zaobera rovnomerne najsilnejsini ncstrannyin testom a jeho odvodonim obocnc a aj ])re parn.met.ro normalneho ro/,delenia. KlYieove .slov;i: testovanie hypotez, najsilnejsi test, rovnomerne naj.silnejsi test, rovnomerne najsilnejsi nc.stranny test Title: Uniformly most powerful test. Uniformly most powerful unbiased test Author: Vladimira Seckarova Department: Department of Probability and Mathematical Statistics Supervisor: Mgr. .lozef .luricek Supervisor's e-mail address: jurijlam@artax.karlin.mff.cuni.cz Abstract: In this work we study problems of hypotheses testing, particularly exis- tence of the uniformly most powerful and the uniformly...
|
|
|
Estimation of the Location of Zeros of Regression Functions
Juríček, Jozef ; Zvára, Karel (referee) ; Hlávka, Zdeněk (advisor)
The main interest of this master thesis is the estimation of location of zeros of the regression function and its derivatives by the parametric and nonparametric method. The first section includes either linear and nonlinear regression model of the parametric methods. The estimators are then based on the estimates of parameters. The second part includes nonparametric regression model - kernel estimators of the regression function and its derivatives investigated by Gasser and Müller. Especially, the limit distributions of the estimators of zeros and the choice of smoothing parameter and kernel function are studied. Confidence bands for zeros of regression function and its derivatives are constructed in both sections. Models are studied with independent as well as correlated errors. This master thesis o®ers examples to particular sections that are computed with software R and also sources of some programmed functions.
|
guest ::
RSS feed.